FACULTIES OF HUMANITIES
Academic year: 2014-2015
An electro-encephalography study on Dutch-Papiamento codeswitching production
“Een úniko studie” or “een studie úniko”?
Myrthe Wildeboer
S1028596
Supervisor: Dr. M. Carmen Parafita Couto
Thesis submitted in partial requirement for the degree of
RESEARCH MASTER IN LINGUISTICS
i
Abstract
Intra-sentential code-switching in Dutch-Papiamento bilingualism may create a conflict within
the determiner phrase, because in Dutch the adjective precedes the noun (1), whereas in
Papiamento the adjective follows the noun (2).
1. Het
rode
huis
[Dutch]
2. E
kas
kòrá
[Papiamento]
“The red house”
The Matrix Language Framework (MLF – Myers-Scotton, 1993) suggests that the matrix
language will provide the grammatical frame and that the embedded language will supply some
content elements. The matrix language will thus determine the word order in a code-switched
determiner phrase. In the case of Dutch-Papiamento intra-sentential code-switching, the MLF
will predict an [adjective-noun] order when the matrix language is Dutch, and the MLF will
predict a [noun-adjective] order when the matrix language is Papiamento. However, the MLF
does not make a prediction about the origin of the adjective or the noun. Thus, when the matrix
language is Dutch, both combinations of [Dutch adjective-Papiamento noun] and [Papiamento
adjective-Dutch noun] would be possible according to the MLF. The same principle applies for
Papiamento as the matrix language, both language combinations [Papiamento noun-Dutch
adjective] and [Dutch noun-Papiamento adjective] would be possible according to the MLF.
The aim of the present study is to test the predictions of the MLF in Dutch-Papiamento codeswitching production. The four code-switching patterns mentioned above were used as
conditions that match the predictions of the MLF (“MLF+ conditions”). Another four
conditions were created by reversing the order of the adjective and the noun in both matrix
language paradigms, to create a violation of the predictions of the MLF (“MLF- conditions”).
A total of eight conditions were used in this study.
The MLF predictions were tested by using an advanced psycholinguistic method, namely
electro-encephalography (EEG). The integration of a psycholinguistic method in a codeswitching experiment is an innovative way of testing the predictions of a theoretical model. In
this study, an EEG signal was recorded while Dutch-Papiamento bilingual speakers conducted
a modified picture naming task. The conditions were analysed by looking at naming latencies
and by looking at the part of the EEG signal following target presentation.
Based on results of previous picture naming tasks (Christoffels, Firk & Schiller, 2007;
Rodriguez-Fornells, Van Der Lugt, Rotte, Britti, Heinze & Münte, 2005; Misra, Guo, Bobb &
ii
Kroll, 2012), I expected slower naming latencies and a more negative waveform for the
conditions that violate the predictions of the MLF. The expected slower naming latencies were
observed in two MLF- conditions: Papiamento adjective followed by a Dutch noun
(Papiamento matrix language) and Papiamento noun followed by a Dutch adjective (Dutch
matrix language). The expected negative waveform was observed in only one MLF- condition:
Papiamento adjective followed by a Dutch noun (Papiamento matrix language). Furthermore,
a P300 (with an early peak in the frontal/central area and a later peak in the occipital area) and
a late positive component seem to be elicited in code-switching production. The amplitude of
the P300 peak was higher in the conditions that contain a violation of the MLF, which could be
explained by the higher complexity of the MLF- conditions. The occurrence of the P300 could
be explained in terms of the context-updating theory (Donchin, 1981; Donchin & Coles, 1988)
or the neural inhibition theory (Polich, 2007). On the whole, the results do not provide
conclusive support for the predictions of the MLF.
Key words:
Code-switching, Intra-sentential, Dutch, Papiamento, Matrix Language Framework, Electroencephalography
iii
Abbreviations
These abbreviations are used in the example sentences. Other abbreviations will be explained
in the running text.
A
adjective
ADV
adverb
AUX
auxiliary
DEF
definite
DET
determiner
FEM
feminine
FUT
future
INDEF
indefinite
MASC
masculine
MOD
modal
N
noun
NEG
negation
O
object
PL
plural
POSS
possessive
PRES
present
PRON
pronoun
PRT
particle
Q
question word
S
subject
SG
singular
TIME
time
V
verb
iv
v
Contents
1.
Introduction to code-switching ........................................................................................ 1
2.
The language pair Dutch-Papiamento ............................................................................ 5
3.
4.
5.
6.
2.1.
Dutch ........................................................................................................................... 5
2.2.
Papiamento .................................................................................................................. 7
2.3.
Previous studies on Dutch-Papiamento bilingualism ................................................ 10
2.4.
Previous studies on conflict sites within the determiner phrase ................................ 12
Models of bilingual speech production ......................................................................... 15
3.1.
The Matrix Language Framework ............................................................................. 15
3.2.
Language specific versus language non-specific....................................................... 18
Electro-encephalogram studies ...................................................................................... 23
4.1.
ERP studies into bilingual speech production ........................................................... 23
4.2.
ERP studies and the determiner phrase ..................................................................... 25
4.3.
ERP studies into code-switching ............................................................................... 26
Methodology .................................................................................................................... 31
5.1.
Participants ................................................................................................................ 31
5.2.
Material ...................................................................................................................... 32
5.3.
Procedure ................................................................................................................... 33
5.4.
EEG-recording and data analysis .............................................................................. 34
Results .............................................................................................................................. 37
6.1.
Background questionnaire ......................................................................................... 37
6.2.
Behavioural results .................................................................................................... 41
6.2.1.
Analysis of MLF predictions.............................................................................. 41
6.2.2.
Analysis of code-switching patterns .................................................................. 42
6.3.
Electrophysiological results ....................................................................................... 44
6.3.1.
Analysis of MLF predictions.............................................................................. 44
6.3.2.
Analysis of code-switching patterns .................................................................. 57
6.4.
Summary of the results .............................................................................................. 63
vi
7.
Discussion......................................................................................................................... 65
8.
Conclusion ....................................................................................................................... 73
References ............................................................................................................................... 75
Appendices .............................................................................................................................. 81
Appendix A – The pictures (Dutch name / Papiamento name) ............................................ 81
Appendix B – The experimental sentences .......................................................................... 83
Appendix C – The information form (in Dutch) .................................................................. 85
Appendix D – The consent form (in Dutch) ......................................................................... 87
Appendix E - The background questionnaire in Dutch ........................................................ 88
Appendix F – The background questionnaire in Papiamento............................................... 92
Appendix G – The Grand Average waveforms of the age-related analysis ......................... 96
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
1
1. Introduction to code-switching
In the literature, there is no consensus about the definition of code-switching. Terms used in the
literature are, among others, code-switching1, code-mixing and language switching. However,
language switching and code-switching do not refer to the same type of process and the results
of a language switching experiment are often misinterpreted as evidence for code-switching.
Costa and Santesteban (2004) conducted a language switching task with Spanish-Catalan
bilinguals and Korean-Spanish bilinguals. Participants were instructed to choose the language
of the response according to the color of the picture. As a result, participants focused on one
language at a time and inhibited the response in another language. For these two groups
switching from their second language to their first language was more difficult than vice versa
(Costa & Santesteban, 2004; replication of the asymmetrical switching costs found in Meuter
& Allport, 1999).
In contrast to language switching, code-switching is the integration of the grammars of
both languages in the same conversation. According to Meisel (1994) code-switching is seen
as “the ability to select the language according to the interlocutor, the situational context, the
topic of the conversation, and so forth, and to change languages within an interactional sequence
in accordance with sociolinguistic rules and without violating specific grammatical constraints”
(Meisel, 1994: 415). Meisel’s definition presumes that grammatical rules constraint codeswitching. Myers-Scotton (1992) also presumes that code-switching is not random, however,
she also suggests that the two languages should not be considered equal (Myers-Scotton, 1992).
This asymmetry between the two languages will be explained in more detail in chapter 4, where
the grammatical constraints of code-switching will be discussed.
The use of two languages by the same speaker within the same conversation is termed
code-switching and can be divided into two main types: intra-sentential and inter-sentential
code-switching. Code-switching at sentential boundaries is generally referred to as intersentential code-switching, while switching below sentential boundaries is called intra-sentential
code-switching (MacSwan, 2000). (1) is an example of intra-sentential code-switching in
Welsh-English (Deuchar, 2006) and (2) is an example of inter-sentential code-switching in
English-Swahili (Myers-Scotton, 1993a).2
1
Alternate spellings in the literature include codeswitching and code switching. In this study code-switching is
used throughout, except in direct quotes from articles in which a different spelling is used.
2
Two types of fonts (normal-bolded) are used in both the language pair and the example sentences, to illustrate
which parts of the example sentences belong to which language of the language pair.
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M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
(1) Achos fod
Because be
gen
ti
dy
silk
handkerchief yn
dy
to
PRON.2SG
your
silk
handkerchief in
your
boced
pocket
“Because you have your silk handkerchief in your pocket”
(Welsh-English, Deuchar, 2006: 1995)
(2) That’s too much. Sina pesa.
“That’s too much. I have no money”
(English-Swahili, Myers-Scotton, 1993a: 41)
Sometimes a third type of code-switching is identified, referred to as extra-sentential
switching (Poplack, 1980) or tag-switching (Cantone, 2007). This type of switching involves
an utterance in one language and a tag or an interjection from another language, like the German
‘”weisst du” (3) or the Italian “capisci” (4) (Cantone, 2007).3
(3) Oggi Sara era al nuovo negozio, weisst du?
“Today Sara was at the new shop, you know?”
(Italian-German, Cantone, 2007: 58)
(4) I was happy about that, capisci?
“I was happy about that, do you understand?”
(English-Italian, Cantone, 2007: 58)
The focus of this study is on intra-sentential code-switching, and therefore, the examples
that will be used, are examples of intra-sentential code-switching. The intra-sentential type of
code-switching demonstrates how different grammars of two languages can interact within the
same clause. The language pair Dutch-Papiamento will be studied, because the grammars of
Dutch and Papiamento may create a conflict for intra-sentential code-switching. Thus, when
not further specified, code-switching refers to intra-sentential code-switching. Some authors
(such as Treffers-Daller, 1994) make this distinction by using the term code-mixing, while
3
Bolded text in the example sentences is used to highlight the tag or interjection.
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
3
others use these terms interchangeably. I have chosen not to draw a distinction between the two.
To avoid confusion, I will stick to one term: code-switching.
Now that I have explained the term code-switching, I will explain the point of views in
which code-switching has been studied. Code-switching has mainly been studied from a
linguistic and a sociolinguistic point of view (Backus, 2009). While linguists mainly focus on
the structural aspects of code-switching and the formulation of constraints based on a theoretical
model (e.g. Woolford, 1983), sociolinguists have mainly focused on the social motivations and
social correlates of code-switching (e.g. Auer, 1988). Less research is done from a
psycholinguistic point of view, in which researchers concern themselves with questions about
how the linguistic system of a bilingual is stored and organized in the cognitive system, and
how it is accessed in in language production and comprehension (Backus, 2009). Backus claims
that “investigating usage patterns in corpora generates hypotheses about competence that
should be tested, if possible, with psycholinguistic experiments” (Backus, 2009: 308) and he
suggest that there should be more interdisciplinary approaches to code-switching. In this present
study, I use an interdisciplinary approach to study Dutch-Papiamento code-switching
production. A psycholinguistic method will be used to test the predictions of a theoretical
model.
This study will have the following outline: in chapter 2 the grammars of both languages
will be discussed, which will make the grammatical conflict more clear. The models of bilingual
speech production will be discussed in chapter 3. The discussion of the bilingual speech
production models will show how elements from multiple languages can be combined. The
main focus will be on the Matrix Language Framework model. The aim of this study is to test
the predictions of this Matrix Language Framework by using electro-encephalography.
Background information about previous electro-encephalography studies will be provided in
chapter 4. Subsequently, the methodology (chapter 5) and results (chapter 6) of this present
study will be illustrated and there will be a discussion (chapter 7) and conclusion (chapter 8) of
the findings at the end.
4
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
5
2. The language pair Dutch-Papiamento
In order to study code-switching between Dutch and Papiamento, the grammars of Dutch and
Papiamento need to be explained first. The linguistic situation, the basic word order and word
order of the determiner phrase of Dutch and Papiamento will be discussed in more detail below.
2.1. Dutch
Linguistic situation
Dutch is a west-Germanic language and is the official language of the Netherlands, Flanders,
Suriname and the Leeward group of Islands of the Dutch Antilles (Janssens & Marynissen,
2005). However, these are not the only countries in which speakers of the Dutch language
reside. Although Indonesia has been independent since 1948, there are still many older people
who were educated in Dutch in the former Dutch East Indies and who still speak the language
very well. Before Surinam was granted independency, many of the Creoles, Indians, Negroes,
Javanese and Chinese chose to move to Holland and these people have also been linguistically
integrated. In the 1950s, thousands of people left the Netherlands to settle in Canada, U.S.A.,
Australia, South Africa and New Zealand and so speakers of Dutch can be found across the
world. In terms of number of speakers, Dutch is the third largest Germanic language after
English and German (Donaldson, 1983). The Dutch language is considered to have originated
from various Germanic dialects spoken in the northern part of the Netherlands, mostly of (low)
Frankian origin. These dialects were mutually intelligible, but no standard form of speech
existed. Due to important trade cities in the west, like Amsterdam and Antwerp, a standard form
of the language began to develop. From the late Middle Ages on, this standard form started to
evolve into the “standard Dutch” as we know it today (Shetter, 1994). Nowadays, Dutch has
approximately 23 million speakers, most of them reside in the Netherlands (16 million),
Flanders (6 million) and Suriname (400.000) (Janssens & Marynissen, 2005).
Basic word order
There is strong evidence that the basic word order in Dutch is subject-object-verb. The
following examples show this word order in main clauses with modal (5a) and time auxiliaries
(5b) and embedded sentences with finite verbs (6) (Jordens, 1988).
6
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
(5) a.
Karel
wil
Hans een
boek
Charles
AUX.MOD
Hans
book give
S
DET.INDEF
O
geven
V
“Charles wants to give Hans a book”
b.
Karel
heeft
Hans een
boek
Charles
AUX.TIME
Hans
book given
S
DET.INDEF
O
gegeven
V
“Charles has given Hans a book”
(Dutch, Jordens, 1988: 42)
(6) Ik
1.SG
zie
dat
Karel
Hans een
boek
see.1SG
ADV
Charles
Hans
book give.3SG
S
O
DET.INDEF
geeft
V
“I see that Charles gives Hans a book”
(Dutch, Jordens, 1988: 43)
Dutch main clauses may be introduced by other elements than the subject. In that case, the
finite verb precedes the subject and immediately follows the first constituent, as in the case of
question words (7a,b) and adverbs (8a,b) (Zwart, 1993).
(7) a.
*Waarom
Q
b.
Waarom
Q
Jan
kust
Marie?
Jan
kiss.3SG
Marie
S
V
kust
Jan
Marie?
kiss.3SG
Jan
Marie
V
S
“Why does John kiss Mary?”
(Dutch, Zwart, 1993: 45)
(8) a.
*Weer
Jan
kust
Marie
ADV
Jan
kiss.3SG
Marie
S
V
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
b.
Weer kust
ADV
Jan
Marie
kiss.3SG
Jan
Marie
V
S
7
“Again John kisses Mary”
(Dutch, Zwart, 1993: 45)
The Dutch finite verb is always in the second position in the main clause. This phenomenon
is called verb-second. Therefore the syntax of Dutch is referred to as subject-object-verb with
verb-second (Zwart, 1993).
The determiner phrase
In the present study, the focus is on the determiner phrase, for which the word order is
determined by the placement of an adjective and a noun. Dutch only allows for pre-nominal
adjectives4 (9a-b) (Zwart, 2011).
(9) a.
b.
*Het
schip
snelle
DET.DEF
ship
fast
N
A
Het
snelle
schip
DET.DEF
fast
ship
A
N
“The fast ship”
(Dutch, Zwart, 2011: 8)
2.2. Papiamento
Linguistic situation
Papiamento is a creole language that is spoken by the native inhabitants of Curaçao, Bonaire
and Aruba. These three islands form the so-called Leeward Islands; together with the Dutch
Windward Islands – Saint Martin, Saba and Statia – they form the Dutch Antilles. The Dutch
Antilles are located in the Caribbean Sea off the Venezuelan coast (Kook & Narain, 1993). The
4
In Dutch, a distinction can be made between predicative and attributive adjectives (Zwart, 2011). However,
predicative adjectives are not part of the determiner phrase. In sentences with a predicative adjective (“the ship is
fast”), there is a predicational relation between a determiner phrase and an adjective. Attributive adjectives are
part of the determiner phrase, therefore, when I talk about adjectives, I am talking about attributive adjectives.
8
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
home languages of the six islands differ greatly. More than 80% of the population of the Dutch
Windward Islands make use of an English-based creole as their home language, while more
than 80% of the population of the Leeward Islands uses a creole with a mixed Portuguese and
Spanish origin, namely Papiamento (Todd Dandaré, 1985). Papiamento has approximately
190.000 native speakers (de Palm, 1985). Although there are some differences in the types of
Papiamento spoken on Curaçao, Bonaire and Aruba, people from these islands can still
communicate with each other in this creole language. The language spoken in Aruba is referred
to as Papiamento and the language spoken in Bonaire and Curaçao is referred to as Papiamentu.
The differences can be seen in the intonation, the lexicon, but mainly in the orthography.
Papiamentu from Curaçao and Bonaire has a more phonologically based orthography, while
Papiamento from Aruba has a more etymologically based orthography (Kook & Narain, 1993).
For the remainder of this study, the name Papiamento will be used to refer to the language
spoken in Aruba, Bonaire and Curaçao.
The six islands are former Dutch colonies, therefore Dutch remains the official language.
Nevertheless, most inhabitants of the Leeward Islands see Dutch as a foreign language. Most
newspapers are written in Papiamento and most radio stations broadcast in Papiamento.
Inhabitants use Dutch very little in their daily life, whereas the use of Dutch is vital in the
Netherlands (Muysken, Kook & Vedder, 1996). There is great variety in the degree of
bilingualism of the Dutch-Papiamento speakers.
Basic word order
Papiamento is considered a (creole) language with a very strict subject-verb-object word order,
see examples (10) and (11) (Kook & Geert, 1988).
(10) Mi
ta
un
hende
1.SG
be.SG
DET.INDEF
person
S
V
O
“I am a person”
(Papiamento, Kook & Geert, 1988: 49)
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
(11) Bo
ta
mi
amigo
2.SG
be.2SG
POSS.1SG
friend
S
V
O
9
“You are my friend”
(Papiamento, Kook & Geert, 1988: 49)
The subject in Papiamento always precedes its predicate, even in question sentences (12),
where Dutch would reverse the two, as was shown in example (7) (Muysken, Kook & Vedder,
1996).
(12) Bo
ta
2.SG
aki
ei?
here
there
kere
ku
nan
lo
bai
bende e
sapa
believe
ADV
3.PL
FUT
go
sell
shoes
S
V
DET.DEF
O
“Do you believe that they will sell these shoes there?”
(Papiamento, Muysken, Kook & Vedder, 1996: 492)
The determiner phrase
With regard to the determiner phrase, Papiamento allows for post-nominal adjectives (13a,b)
(Kook & Geert, 1988).
(13) a.
Un
kas
DET.INDEF
house clean
N
limpi
A
“A clean house”
b.
Un
sapatu
DET.INDEF
shoes brown
N
maron
A
“A brown pair of shoes”
(Papiamento, Kook & Geert, 1988: 102)
Yet, there is a group of Papiamento adjectives that can appear either in pre-nominal
position or in post-nominal position, depending on the meaning of the sentence. This group
10
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
consists of the adjectives bon (‘good’), mal (‘bad’), gran (‘big’), dushi (‘attractive’), nèchi
(‘beautiful’), bonita (‘beautiful’) and mahoso (‘ugly’). According to Kook and Geert (1988:
103), “if you put dushi, nèchi, bunita or mahos in pre-nominal position this will emphasize the
dushi-nèchi/bunita/mahos-character of the noun”. A few of these opposing positions are shown
in Table 1 (Kook & Geert, 1988: 103).
Table 1. Three Papiamento adjectives in pre-nominal and post-nominal position.
Pre-nominal position
Un
bon
hende
DET.INDEF
good person
“a good person”
Post-nominal position
Un
hende bon
DET.INDEF
person good
“a person with luck”
Un
mal
hende
Un
hende malu
DET.INDEF
bad
person
DET.INDEF
person bad
“a bad person”
“a sick person”
Un
gran hende
Un
hende grandu
DET.INDEF
big
DET.INDEF
person big
“a famous person”
person
“a big person”
Post-nominal adjectives in Papiamento would lead to a phrase like “biña còrá” (lit. wine
red), while Dutch speakers would say “rode wijn” (lit. red wine). Dutch-Papiamento is an
interesting language pair to investigate within the context of the determiner phrase, because it
creates a conflict site in code-switching. One language contains pre-nominal adjectives and the
other language contains post-nominal adjectives, so it is unclear how a Dutch-Papiamento code
switch may look like with such a conflicting word order. Now that the grammars of Dutch and
Papiamento have been explained and the word order conflict has been made clear, I will explain
the previous studies that have looked at Dutch-Papiamento code-switching.
2.3. Previous studies on Dutch-Papiamento bilingualism
Code-switching between Dutch and Papiamento has not been extensively studied yet. Previous
code-switching research does include some studies on Dutch. Researchers have studied some
language contact situations within the Netherlands (Dutch-Turkish - Backus, 1993; DutchMoroccan Arabic, Nortier, 1990) and outside the Netherlands, like Dutch-English in Australia
(Clyne, 1978), Dutch-Malay bilingualism in Indonesia (Huwaë, 1992), Sarnami HindustaniDutch (Kishna, 1979) and French-Dutch bilingualism in Belgium (Treffers-Daller, 1994).
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
11
However, only a small number of studies have looked at code-switching in Dutch-Papiamento
bilingualism (Muysken, Kook & Vedder, 1996; Vedder, Kook & Muysken, 1996; Gullberg,
Indefrey & Muysken, 2009; Parafita Couto & Gullberg, 2015).
Two studies were done by Muysken, Kook and Vedder in 1996. In one study, Muysken et
al. (1996) focussed on 1) the relationship between code-switching and language proficiency 2)
the structural properties of code-switching and 3) the influence of borrowing on language
change. They found that intra-sentential code-switching characterizes high proficiency in both
languages and that insertional switches were the most common switches with Papiamento as
the matrix language (Muysken et al., 1996). In the second study they looked at language choice
and functional differentiation in bilingual reading (Vedder et al., 1996). Mothers were asked to
read three picture books to their child: one in Dutch, one in Papiamento, and one without text.
Results of their study showed that language choice was related to the text and contents of the
book, as well as to restrictions imposed by the language proficiency in both languages of the
mothers and children. The mothers categorized counting as school-related and tended to use
Dutch to count (Vedder et al., 1996).
Muysken was also involved in a study on Dutch-Papiamento code-switching that used an
adapted version of the Director-Matcher elicitation task5 (Gullberg, Indefrey and Musyken,
2009), which was designed to elicit noun phrases consisting of determiners, color adjectives
and nouns. Gullberg et al. (2009) used this task successfully in the Dutch-Papiamento bilingual
community in the Netherlands. In order to get a complete picture of code-switching, an
integration of corpus and naturalistic data, grammaticality judgments and neurolinguistic
evidence is necessary (Gullberg et al., 2009). However, these three types of data are usually
studied separately. For example, Parafita Couto and Gullberg (2015) have looked at corpora of
three different bilingual communities to find the switching patterns in complex, modified noun
phrases. The first corpus is the Dutch-Papiamento corpus, of which the data is collected in the
Netherlands, and this corpus can now be accesses at The Max Planck Institute for
Psycholinguistics6. The second corpus is the Welsh-English ‘Bangor Siarad’ corpus7 (Deuchar,
Davies, Herring, Parafita Couto & Carter, 2014), which includes data from speakers across
Wales. The third corpus is the English-Spanish ‘Bangor Miami’ corpus8, of which the data is
collected in Miami, Florida (Deuchar et al., 2014). The latter two corpora are both available at
5
This task is a referential communication task (Yule 1997) in which two speakers participates. The Director
instructs the Matcher to do something.
6
http://www.mpi.nl/resources/data/browsable-corpora-at-mpi
7
http://bangortalk.org.uk/speakers.php?c=siarad
8
http://bangortalk.org.uk/speakers.php?c=miami
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M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
the Centre for Research on Bilingualism at the Bangor University (links are provided in the
footnotes). Results of their corpus study showed that the most frequent word order is
determiner-adjective-noun9 (Parafita Couto & Gullberg, 2015). Within a determiner-adjectivenoun construction, the speakers tended to produce the determiner in one language and both the
adjective and the noun in another language. In the case of Dutch-Papiamento, this resulted in a
Papiamento determiner followed by a Dutch adjective and a Dutch noun. Switches between the
adjective and the noun10 (e.g. Determiner-Adjective-Noun, Determiner-Noun-Adjective,
Determiner-Noun-Adjective) were also present in the Dutch-Papiamento corpus, but occurred
less frequently (Parafita Couto & Gullberg, 2015). Three main patterns were observed:
Papiamento-Dutch-Papiamento,
Papiamento-Papiamento-Dutch
and
Papiamento-Dutch-
Dutch. The first two patterns are patterns where the conflicting word order comes into play. If
the adjective and the noun are from the same language, they will follow either the Dutch prenominal order or the Papiamento post-nominal order. But what happens when both word orders
are available? Parafita Couto and Gullberg (2015) have provided some insight in which codeswitching patterns emerge in naturalistic data, however, more evidence (from grammaticality
judgement tasks and neurolinguistic experiments) is necessary in order to get a better
understanding of code-switching. The present study is the next step in trying to integrate corpus
data in a neurolinguistic experiment. This research builds on the results found by Parafita Couto
and Gullberg (2015) and may provide neurolinguistic evidence for the switching patterns found
in their corpus study. The focus will be on the code-switching patterns with a switch between
the adjective and the noun, because that is where the grammars of Dutch and Papiamento may
create a conflict. Now, I will look at previous studies regarding (similar) conflict sites within
the determiner phrase.
2.4. Previous studies on conflict sites within the determiner phrase
Research on intra-sentential code-switching has shown that determiner phrases represent one
of the most frequent switching points in bilingual speech (Poplack, 1980; Cantone, 2007). As
stated in previous sections, intra-sentential code-switching may create a conflict site within the
determiner phrase. In the case of Dutch-Papiamento bilingualism, the grammatical systems of
the two languages differ in terms of word order, but other differences (and thus conflicts) can
be found as well. For example, intra-sentential code-switching between a determiner and a noun
9
Welsh and Spanish, like Papiamento, both contain post-nominal adjectives as well.
Bolded text indicates Papiamento, italicized text indicates Dutch
10
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
13
with gender encoded languages may create a conflict in gender assignment. If the grammatical
systems of both languages encode gender, then the bilingual speaker will encounter a conflict
and has to cope with two options: 1) use the gender of the noun actually realized or 2) use the
gender of the equivalent noun in the language of the determiner (Eichler, Hager & Müller,
2012). For the purpose of this study, I will not discuss other conflict sites in further detail and
only focus on work that has been done on the word order conflict.
Parafita Couto, Deuchar and Fusser (2015) looked at Welsh-English bilingualism, which
has a similar word order conflict in the determiner phrase. English, like Dutch, has pre-nominal
adjectives (red wine), while Welsh, like Papiamento, has post-nominal adjectives (gwin coch –
lit. wine red). In the field of code-switching research there have been two mainstream
theoretical models that make predictions about the possible combinations of elements: the
Minimalist Program (Chomsky, 1995) and the Matrix Language Framework (Myers-Scotton,
1993b). The principles and premises of these models will be discussed in more detail in the next
chapter. Parafita Couto et al. (2015) used a multi-task approach, consisting of naturalistic corpus
data, an elicitation task, and an auditory judgment task, to find out which theoretical model
would make the best predictions regarding the word order conflict in code-switched determiner
phrases. Data from the grammaticality judgment task showed that this type of task was not very
useful in code-switching research Data from the naturalistic corpus and the elicitation task,
however, were compatible with each other and both yielded support for the predictions of the
Matrix Language Framework (Parafita Couto et al., 2015). Parafita Couto et al. (2015) suggest
that neuroscientific evidence could make a useful contribution here and I am trying to follow
that suggestion in this present study. Nevertheless, a clear understanding of the theoretical
models is needed first. In the next chapter, I will discuss the principles and premises of the
theoretical models in further detail and I will explain the language selection process during
bilingual speech production.
14
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
15
3. Models of bilingual speech production
In the previous chapter, it was queried what a Dutch-Papiamento code-switch would look like
and to what extent such a code-switch would be grammatical. The two mainstream theoretical
models in the field of code-switching research have already been briefly mentioned in the
previous chapter, namely the Minimalist Program (MP – Chomsky, 1995) and the Matrix
Language Framework (MLF - Myers-Scotton, 1993b). For the purpose of this study, I will only
focus on the predictions of the MLF, because testing the predictions of both models would be
too difficult to accomplish in just one experiment. Besides, previous research has found more
evidence in favour of the MLF (Parafita Couto et al., 2015) and therefore I decided to solely
test the predictions of the MLF.
In short, the Minimalist Program assumes a so-called ‘null theory’. This null theory states
that nothing but the requirements of both languages restricts code-switching. Considering the
only requirement both language have is the order of the adjective and the noun, the Minimalist
Programs suggest that the language of the adjective determines the word order (see Cantone &
MacSwan, 2009). Jake, Myers-Scotton and Gross (2002) argue that the minimalist program on
its own is not sufficient to explain what occurs in code-switching. From now on, the present
study will only focus on the MLF and a detailed description of this model will now follow.
3.1. The Matrix Language Framework
According to Myers-Scotton (2006) the outcome of a bilingual speaker is not a result of equal
participation of both language varieties. One language will provide the grammatical frame,
called the matrix language, and the other language will supply some content elements, called
the embedded language. This idea about an asymmetry between two participating languages
was already risen in 1985 (Joshi, 1985). The prediction was that all items from closed classes,
like determiners, prepositions, quantifiers, would come from the matrix language, while item
from open classes, like nouns, verbs and adjectives, would come from either the matrix
language or the embedded language (Joshi, 1985). To account for this asymmetry the MLF was
proposed by Myers-Scotton (1993b).
The MLF has three main premises about the matrix and embedded language. The first
premise is that there is an unequal participation of the matrix language and the embedded
language in a constituent structure. The second premise yield that the matrix language and the
embedded language do not provide all types of morphemes equally. The third and last premise
16
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
entails that both languages of a bilingual speaker are always “on” during code-switching. This
idea that both languages are always active in a bilingual’s mind replaced the idea that there is a
language switch, in which a bilingual speaker can switch off one language and switch on
another language (Myers-Scotton, 2006). It has been argued by Kroll and Dijkstra (2002) that
there is evidence for non-selective access to words in both languages, which means that there
is activation in both languages when a bilingual speaker needs to select or produce a word in
one language only. This was tested with Dutch-English bilinguals. The bilinguals had to name
pictures in English (L2) with auditorily presented distractor words. When the distractor word
was a word that sounded like the L1 name of the picture (“phono-translation”), the results
showed interference in naming the picture. The results of the phono-translations revealed a
different time course than the results for the phonologically related distractor words, which
indicates that the translation equivalent is active through the stage of selecting an initial
candidate, but one that is not yet phonologically specified (Kroll & Dijkstra, 2002).
Furthermore, the MLF yields two important principles that help to determine the matrix
language of a (code-switching) clause. These principles are the morpheme order principle and
the system morpheme principle. The morpheme order principle says that in a mixed language
constituent the surface morpheme order should be that of the matrix language. The second
principle, the system morpheme principle, claims that in mixed language constituents all system
morphemes which have grammatical relations external to their head constituent will come from
the matrix language (Myers-Scotton, 1993b, 2002). Example (14) illustrates the application of
both MLF principles in a Welsh-English sentence (Deuchar, 2006).11
(14) Mae
be.3SG.PRES
o-‘n
PRON.3SG-PRT
reit
quite
camouflaged yn
dydi
camouflaged
NEG-be.3SG.PRES
PRT
“He’s quite camouflaged isn’t he?”
(Welsh-English, Deuchar, 2006: 1987)
According to the morpheme order principle the matrix language would be Welsh. The verb
‘mae’ is in clause-initial position, which reflects the verb-subject-object word order of Welsh.
According to the system morpheme principle the matrix language is also Welsh, because the
11
Two types of fonts (normal-bolded) are used in both the language pair and the example sentences, to illustrate
which parts of the example sentences belong to which language of the language pair.
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
17
verb (with third person singular subject marking) and subject pronoun (third person masculine
singular) also come from Welsh.
Example (14) contains a single occurring word as a code switch. Many researchers would
call these single occurring words ‘borrowings’, whereas the MLF interprets such words as a
code switched element in a mixed constituent (Myers-Scotton, 2006). A single-word switch is
often referred to as a borrowing (“nonce borrowings” – Poplack, 1980), because some
researchers claim there is a different mechanism at work. In the case of borrowing, only the
integrity of the grammar of the recipient language was respected, while in code-switching the
integrity of the grammar of both the donor and the recipient languages need to be respected. In
most example sentences the word order, as far as the single occurring word is concerned, is the
same for the two languages. In this present study, there is a conflict regarding word order. So,
what word order do singly occurring embedded language forms follow? The MLF, especially
the morpheme order principle, claims that the matrix language order determines the word order
in such a conflict site. For example, English has a typical subject-verb-object word order and
in Croatian the verb typically comes last in a clause. The position of the verb supervise occurs
in the final position (15), demonstrating that Croatian provides the frame of the clause and is
therefore the matrix language of that sentence (Myers-Scotton, 2006).12
(15) Ne
on
radi
taj
posao I
ja
njega supervise
no,
he
does
that
job
I
him
and
supervise
“No, he does the job and I supervise him”
(Croatian-English, Myers-Scotton, 2006: 256)
In Dutch and Papiamento there is a conflict between pre-nominal adjectives versus postnominal adjectives. According to the morpheme order principle of the MLF the PapiamentoDutch constructions with post-nominal adjectives, like “biña rode” and “wijn còrá”, would only
be possible if the matrix language is Papiamento; constructions with pre-nominal adjectives,
like “rode biña” and “còrá wijn”, would only be possible if the matrix language is Dutch.
Deuchar (2006) – among other researchers – has shown that the morpheme order principle
and the system morpheme principle are supported in Welsh-English code-switching data. This
12
Two types of fonts (normal-bolded) are used in both the language pair and the example sentences, to illustrate
which parts of the example sentences belong to which language of the language pair
18
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
support proves that a code switch is not a random mix of language varieties, but that it has –
contrary to the belief of many speakers – a principled basis (Myers-Scotton, 2006). All in all,
the MLF emphasizes what happens on the abstract level of language production. However, there
is still some discussion about one of the premises of the MLF, stating that both languages are
active during production.
The early view was that the languages of a bilingual were not both activated at the same
time. The idea of “turning on” one language and “turning off” the other language has been
replaced by a general agreement that both language are always “on” to some extent (MyersScotton, 2006). According to Grosjean (1997), there is a continuum along which speakers can
move, from a “bilingual mode” to a “monolingual mode”. A bilingual’s motivation to move
from one mode to the other depends on language proficiency, task and situation (Grosjean,
1997). The presumption among researchers is that both languages are activated at some point
whenever a bilingual speaks, even when they speak in one language (non-selective access –
Kroll & Dijkstra, 2002). Exactly how does a bilingual speaker select the language of their
choice? Imagine an adjective-noun code-switch, are adjectives from both languages activated
and does one adjective need to be inhibited? Or is just one of the two adjectives considered for
selection?
The literature provides no consensus regarding the manner and locus of language selection
in bilingual speech. It has been argued that this lack of agreement is a result of the different
experimental paradigms used in research on bilingual speech production (Hoshino & Thierry,
2014). Some theories on bilingual language access in speech production presuppose that the
lexicon of both languages, the one in use and the one not in use, are activated through a common
semantic system (Costa & Caramazza 1999). The question arises of how bilingual speakers
select the proper lexical items when both lexicons are activated. Two models have been
proposed in order to solve this issue: the language specific model and the language non-specific
model. The next section will explain the two models.
3.2. Language specific versus language non-specific
The first model is the language non-specific model, which proposes that candidates from both
the target and the non-target language compete for selection and that the activation of the nontarget candidates needs to be inhibited (asymmetrical pattern of switching costs - Meuter &
Allport, 1999). These so-called inhibitory models need a mechanism that inhibits the activation
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
19
of the non-target language (Green, 1986; Meuter & Allport, 1999). A picture word interference
experiment was conducted by Hermans, Bongaerts, De Bot and Schreuder (1998) to test the
prediction of this model. Dutch-English bilinguals were instructed to name pictures in English
while a Dutch distractor word appeared on the screen. The distractor words were either
phonologically related, semantically related, unrelated or phonologically related to the Dutch
name of the picture. The last type of distractor may interfere with the lemma selection process
by activating the not-to-be-selected Dutch lemma, which would make it harder to select the
English lemma. Their results demonstrated that during the initial stages of lexical access –
accessing the English name of the picture – the Dutch picture name is also activated. Hermans
et al. (1998) conclude that bilingual speakers can’t prevent their first language from interfering
with the production in their second language and explains this as evidence for the language nonspecific model.
The second model is the language specific selection model, which claims that alternative
candidates are active but only candidates from the intended languages are considered for
selection. Costa, Miozzo and Caramazza (1999) have used the picture word interference
paradigm to test whether the there is a language specific or a language non-specific lexical
access in bilinguals. Participants were Catalan-Spanish bilinguals, who had to name pictures in
Catalan with a distractor word in either Catalan (same language pairs) or Spanish (different
language pairs). Distractors could either be the name of the picture or an unrelated name. A
facilitation effect was found in the conditions where the distractor was the name of the picture,
in both the same language pairs and the different language pairs. Though the facilitation effect
was larger in the same language pairs. Costa et al. (1999) saw these outcomes as evidence for
a language specific model, because only the words of the target language were considered for
lexical selection.
In a later study by Costa and Caramazza (1999), they tried to replicate the facilitation effect
found in Catalan-Spanish bilinguals with less balanced Spanish-English bilingual speakers and
English-Spanish bilinguals speaking in their first and their second language respectively.
Twenty-one Spanish-English bilinguals (experiment 1) and twenty-one English-Spanish
bilinguals (experiment 2) took part in a picture word interference experiment. Costa and
Caramazza (1999) used twenty-four pictures and six distractor words for each picture, three
Spanish words and three English words. Results of both experiments showed that when the
picture and the distractor corresponded to the same word, the naming latencies were faster than
when the picture and the distractor referred to two different words. This facilitation effect
20
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
occurred in the same-language pairs as well as in the different-language pairs, yet the effect
was larger for the same-language pairs (Costa & Caramazza, 1999). Thus, the results are in line
with the previous study with Catalan-Spanish bilinguals, but also expand the scope of this
language specific model in three different components. First, the bilingual’s ability to keep the
two languages separate during lexical access in speech production is not affected by the
similarity between the two languages. Catalan-Spanish bilinguals behave similar to SpanishEnglish bilinguals. Secondly, the language specific model also applies to proficient nonbalanced bilingual speakers. Thirdly, the bilingual’s ability to restrict lexical selection to only
one lexicon also applies when speaking in a second (non-dominant) language (Costa &
Caramazza, 1999).
To recapitulate, the linguistic situation, basic word order and the determiner phrase of Dutch
and Papiamento have been discussed and the word order conflict within the determiner phrase
has been made clear. There are two main theoretical models in the field of code-switching and
I have chosen to study the predictions of the MLF, based on restrictions of the experiment (e.g.
number of conditions) and results from previous studies. One premise of the MLF is that both
language are activated during production and this activation process was discussed by looking
at language specific versus language non-specific selection models. However, previous
research has provided evidence for the language specific model (Costa et al., 1999; Costa &
Caramazza, 1999) as well as the language non-specific model (Hermans et al., 1998), and thus,
the literature provides no consensus regarding the language selection in bilingual speech
production. In this present study, I want to study code-switching in a neurolinguistic/
psycholinguistic13 manner, since that has been a suggestion for further research in many
previous studies. However, it is not easy to incorporate a psycholinguistic method in an
experiment that tests the predictions of a theoretical model. In the literature, there is no
consensus on whether grammatical theories and language processing models (psycholinguistic
models) describe separate cognitive systems, or whether they are accounts of different aspects
of the same system (Lewis & Phillips, 2015). This present study can contribute to our
understanding of the relationship between theoretical models and psycholinguistic models. I
am going to use electro-encephalography (a psycholinguistic language processing method) to
study the predictions of the MLF (a theoretical model) about Dutch-Papiamento code-
13
In the literature, both terms (neurolinguistic and psycholinguistic) have been used to refer to the mechanisms
in the human brain that control the comprehension, production, and acquisition of language. Therefore, I did not
make a distinction between the two terms in this study as well. To avoid confusion, the term psycholinguistics
will be used from now on.
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
21
switching. The theoretical MLF model and its predictions have already been discussed and the
next step is to look at the method of electro-encephalography. The next chapter will review the
previous electro-encephalography studies on bilingual speech production and code-switching.
22
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
23
4. Electro-encephalogram studies
Event-related potentials (hereafter ERP) are obtained from an electro-encephalography
(hereafter EEG) signal. To subtract ERPs from EEG it is necessary to find the timeslot at which
a certain stimuli or response (called ‘events’) has taken place. The idea behind ERPs is that
“spontaneous activity produced by the brain over the scalp is not synchronized to events such
as stimulus presentation or participant response unless it is related to it.” (Hoshino & Thierry,
2014: 207). Therefore ERP signals reflect the cognitive process which is elicited by the
stimulus. What if the stimulus can trigger a response in more than one language? What type of
cognitive processes will be elicited by such a stimulus? ERP studies into bilingual speech
production and their results will be reviewed in the next section.
4.1. ERP studies into bilingual speech production
Speech production is challenging to study by means of EEG, because activation of the muscles
involved in speech articulation can contaminate the EEG signal and override the signal
originating in the brain. However there is a small group of researchers that have found some
techniques to overcome this difficulty. For example Rodriguez-Fornells, van der Lugt, Rotte,
Britti, Heinze and Münte (2005) have made use of a go/no-go paradigm in combination with
implicit picture naming. A go/no-go task implies that the participants have to make a buttonpress in one condition and withhold their response in another condition. Rodriguez-Fornells et
al. (2005) asked their German-Spanish bilingual participants to press the button if the name of
the picture starts with a consonant and to withhold their response if the picture started with a
vowel. Though they manipulated their stimuli in a way that some pictures started with the same
phoneme (consonant or vowel) in both languages and that some pictures started with different
phonemes in the two languages. Rodriguez-Fornells et al. (2005) found an increased negativity
from 300-600 ms (N200) for the go trials, but not for the no-go trials. This result was interpreted
as an indication of activation and competition between candidates from both languages at a
phonological level.
A similar design was executed by Misra, Guo, Bobb and Kroll (2012). Chinese-English
bilinguals participated in a delayed picture naming task. Pictures had either a red frame,
indicating that the picture had to be named in their first language, or a blue frame, indicating
that the picture had to be named in their second language. Nevertheless it was not the color of
the frame that was important, it was the time of picture naming. Participants were instructed to
24
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
name pictures as soon as possible after three asterisks appeared inside the frame. Based on a
study of Jackson, Swainson, Cunnington and Jackson (2001) these asterisks appeared either at
250 ms or 1,000 ms after the onset of the picture. Overall, the ERPs were more negative when
naming in the first language (L1) followed naming in the second language (L2) (enhanced
N200), while the ERPs were more positive when L2 naming followed L1 (Misra et al., 2012).
The greater negativity of picture naming in the L1 after picture naming in the L2 suggests there
is inhibition of the L1. Findings of both this study and the study of Rodriguez-Fornells et al.
(2005) provide evidence for the language-nonspecific models of bilingual speech production.
Rodriguez-Fornells et al. (2005) and Misra et al. (2012) are one of the few researchers
that have tried to use ERP to study bilingual speech production. In spite of this effort it is
important to mention that the go/no-go task and the delayed picture naming task do not reflect
the actual planning of speech. Participants had to withhold their response in certain conditions
and this requires more cognitive control than immediate naming. There is a lack of knowledge
about the relationship between motor inhibition and speech planning mechanisms (Hoshino &
Thierry, 2014).
In the present study, I want to use a (modified) picture naming task, in which speakers do
not withhold their response, but immediately have name the pictures. Strijkers, Costa and
Thierry (2010) have shown that picture naming latencies can be affected by frequency of the
stimuli, which is called the frequency effect, and by cognate status, which is called the cognate
effect. The frequency effects refers to the observation that high-frequent words show faster
naming latencies than low-frequent words (Strijkers et al., 2010). The cognate effect denotes
that naming latencies for pictures who have a phonologically similar translation in both
languages (e.g., the Spanish-English pair guitarra – guitar) are faster than for pictures whose
translation is phonologically dissimilar in both languages (e.g., the Spanish-English pair perro
– dog; e.g., Costa, Caramazza & Sebastián-Galles, 2000; Christoffels, Firk & Schiller 2007).
Strijkers et al. (2010) tried to establish an electrophysiological index of lexical access in speech
production by exploring the locus of the frequency and cognate effects during overt naming. In
their study, a group of sixteen Spanish-Catalan bilingual participants and a group of sixteen
Catalan-Spanish bilingual participants had to carry out a picture naming task in Spanish,
respectively their L1 and L2. The pictures consisted of 64 line-drawings of familiar objects (e.g.
body parts, buildings, animals). Results of both experiments revealed reliable and robust
frequency and cognate effects, which are in line with results of previous studies (Christoffels et
al., 2007). Besides these confirming results, they also found early ERP effects of frequency and
cognate status in the mean amplitude of the P200, N300 and P300. From 150-200 ms until voice
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
25
onset, ERPs in the high-frequency condition were significantly more negative than those
elicited in the low-frequency condition and ERPs in the cognate condition were significantly
more negative than those elicited in the non-cognate condition. Strijkers et al. (2010) estimate
that lexical access occurs at approximately 180 ms after target presentation and that word
frequency and cognate status influences phonological encoding as well as lexical access. The
results of the study by Strijkers et al. (2010) has shown that frequency and cognate status affect
both naming latencies and ERP data, therefore, I need to keep these factors in mind when I am
creating stimuli for my own experiment.
The studies mentioned above are about bilingual production in general. So far, the most
common result is an enhanced negativity (N200) for the critical conditions. The N200
component is hypothesized to reflect object recognition and categorization (Pritchard, Shappel
& Brandt, 1991; Folstein & van Petten, 2008). The N200 and the P300 (the P300 was found in
the study by Strijkers et al., 2010) appear to be closely associated with the cognitive processes
of perception and selective attention (Patel and Azam, 2005). The P300 is commonly divided
into two sub-components and each of these sub-components has their own scalp distribution
and functional correlates. The first sub-component is the P3a, which is maximal over frontal
areas and reflects the orienting of attention to unexpected or significant events in the
environment. The second sub-component is the P3b, which is maximal over parietal areas and
reflects the updating of the working memory (Donchin, 1981). The ERP components found in
previous bilingual speech production research, all relate to perception and attention, but,
crucially, there is no relation with grammaticality.
After having discussed the studies about general bilingual production, it is also necessary
to discuss what is already known about the combination of ERP and the determiner phrase and
ERP and code-switching.
4.2. ERP studies and the determiner phrase
This study focuses on intra-sentential code-switching within the determiner phrase. As
previously mentioned, the order in a determiner phrase of a given language may be fixed, like
in English “blue car” or in Welsh “car glas” (lit. car blue), or it may vary, like in French
“voiture bleue” (lit. car blue) versus “belle voiture” (lit. beautiful car) (Sanoudaki & Thierry,
2014). Bilingual speakers, who have learned both grammars in early childhood, will
comprehend and produce sentences that are in concordance with the rules of the required
grammar in that situation. Previous electrophysiological studies have focused on the bilinguals’
sensitivity to syntactic violations and how this differs from monolingual sensitivity. These
26
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
studies indicated two factors that play an important role in the way human brains process two
languages, namely the age of acquisition and the level of proficiency (Sanoudaki & Thierry,
2014). The ERP responses of the bilingual brain resemble those of a monolingual brain more
when the exposure to the second language starts earlier and the proficiency is higher (Sanoudaki
&Thierry, 2014). Keep in mind that this statement is about bilingual language comprehension,
and not on production.
Sanoudaki and Thierry (2014) combined the cross-language activation with bilingual
syntactic access. Eighteen monolingual English speakers and sixteen highly proficient WelshEnglish early bilinguals conducted a grammaticality judgment task with written English
sentences. The sentences were either grammatical in English, with a pre-nominal adjective, or
ungrammatical in English, with a post-nominal adjective (which is the Welsh order). An EEGsignal was elicited with a go/no-go design. In the pre-nominal adjective conditions, the no-go
trials elicited a more negative N200 component than the go-trials. This was found for both the
bilingual and monolingual participants. While in the post-nominal adjective conditions, the
results of the bilingual participants exhibited the same changes in the N200 component as in
the pre-nominal adjective conditions, though this was not the case for the monolingual
participants. Results illustrated co-activation of Welsh syntax with English syntax in bilingual
speakers. Regardless of the all-English context, the bilingual mind has spontaneous and implicit
access to the grammars of both languages (Sanoudaki & Thierry, 2014).
4.3. ERP studies into code-switching
Parafita Couto, Boutonnet, Hoshino, Davies, Deuchar and Thierry (submitted) have used ERPs
to investigate the acceptability of nominal constructions in Welsh-English code-switching. To
account for the acceptability of nominal constructions Parafita Couto and her colleagues made
use of the two mainstream theories on code-switching (as mentioned in chapter 3). The MLF
predicts that a pre-nominal Welsh adjective in an English matrix sentence would be acceptable,
but a pre-nominal English adjective in a Welsh matrix sentence would be seen as a violation of
the theory. The predictions of the MP, which are solely based on the language of the adjective,
are the exact opposite. Fifteen Welsh-English bilinguals participated in the experiment, all
highly proficient in both languages. Parafita Couto et al. (submitted) found negative ERP
variation between 280-340 ms in the condition predicted by the MLF to induce a violation.
Negative ERPs in this timeframe are usually interpreted as syntactic violations. Therefore the
findings of Parafita Couto et al. (submitted) were seen as a validation of the predictions of the
MLF. No evidence for the predictions of the MP were found.
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
27
A similar ERP study for Dutch and Papiamento has been carried out as well (Schiller,
Pablos, Sattaur & Parafita Couto, 2013). This study has focused on code-switching
comprehension. Contrary to the Welsh-English study, an N400 was found in the condition
violated by the MP (Schiller et al., 2013). New experiments are being carried out at the moment
to find out which theoretical model makes the best predictions regarding code-switching within
the determiner phrase. The present study is in line with the ongoing research on DutchPapiamento code-switching. Where Schiller et al. (2013) have focused on comprehension, this
present study will focus on production. Will the ERP comprehension results be the same with
respect to code-switching production? The objective of this study is to test the predictions of
the MLF with Dutch-Papiamento bilingual speakers.
The present study
Certain conditions were set up in order to test the predicitons of the MLF. According to the
MLF, the matrix language determines the word order in a code switch. In this case, the matrix
language can be Dutch or Papiamento. So, when the matrix language is Dutch, the word order
will be [adjective-noun]. However, the MLF does not make a prediction about the language of
the adjective or the language of the noun. Therefore both combinations of [Dutch adjectivePapiamento noun] and [Papiamento adjective-Dutch noun] are possible, as long as the order of
the two elements stays the same. For Papiamento, it will be the same principle, both language
combinations [Papiamento noun-Dutch adjective] and [Dutch noun-Papiamento adjective] will
be correct according to the MLF. This results in four conditions that match the predictions of
the MLF:

Matrix language Dutch - [Dutch adjective - Papiamento noun]

Matrix language Dutch - [Papiamento adjective - Dutch noun]

Matrix language Papiamento - [Papiamento noun - Dutch adjective]

Matrix language Papiamento - [Dutch noun - Papiamento adjective]
28
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
Besides the four conditions that match the predicionts of the MLF, I have also used four
conditions that violate the predictions of the MLF. The four conditions mentioned above were
duplicated, but the order of the adjective and the noun were reversed to create a violation.

Matrix language Dutch - [Papiamento noun - Dutch adjective]

Matrix language Dutch - [Dutch noun - Papiamento adjective]

Matrix language Papiamento - [Dutch adjective - Papiamento noun]

Matrix language Papiamento - [Papiamento adjective - Dutch noun]
The first four conditions will be referred to as MLF+ conditions, since they match the MLF
predictions, and the second four conditions will be referred to as MLF- conditions, since they
violate the MLF predictions. Related to these eight conditions, there are three main research
questions (“Q1, Q2, Q3”) and I have stated my hypotheses (“H1, H2, H3”) below each research
question.
Q1. Is there a difference in naming latencies between conditions that match the predictions of
the MLF (MLF+) and conditions that violate the predictions of the MLF (MLF-)? In
addition to this question, is there a difference in naming latencies with regard to the
different types of code-switches (adjective versus noun / Dutch versus Papiamento)?
H1. I expect to find a difference in naming latencies between MLF+ and MLF- conditions, with
slower naming latencies for the MLF- conditions. In the MLF- conditions, the order of the
adjective and the noun does not fit the morpho-syntactic frame presupposed by the matrix
language. First, participants will need to process the mismatch between the word order
presupposed by the matrix language and the word order they are presented with, and
second, they will need to think about how to produce the “new” word order. This
processing and reconfiguration of the production will take time. Somewhat related to this
are the switching costs found in previous naming task studies. Christoffels et al. (2007)
conducted a picture naming task with German-Dutch bilinguals. The color of the picture
signalled the response language. The switch trials occurred unpredictably. Behavioural
data showed that naming latencies were longer for switch trials than non-switch trials. On
the switch trials participants have to inhibit one production, the word in one language, and
come up with another production, the word in another language. Likewise, participants in
the present study will have to inhibit the “correct MLF order” in the MLF- conditions and
produce an order that matches the requirements of the MLF- condition.
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
29
Regarding the naming latency difference for the different code-switches patterns, the
noun has been found to be the most frequently switched element by several researchers
(Wentz, 1977; Timm, 1975). Therefore, I hypothesize that participants will respond faster
when there is a noun inserted than when there is an adjective inserted. Cases where a Dutch
noun is inserted in a sentence with Papiamento as the matrix language and cases where a
Papiamento noun is inserted in a sentence with Dutch matrix language will elicit faster
responses, because noun switches are much more common to the participants than adjective
switches. My goal is to find participants that are highly proficient in both Dutch and
Papiamento and therefore I don’t expect to find a difference in naming latencies between
a Dutch noun insertion and a Papiamento noun insertion.
Q2. Is there a difference in neural activity between conditions that match the predictions of the
MLF (MLF+) and conditions that violate the predictions of the MLF (MLF-)? In addition
to this question, is there a difference in neural activity with regard to the different types of
code-switches (adjective versus noun / Dutch versus Papiamento?
H2. The hypothesis for the electrophysiological data is mainly based on results of previous
picture naming task studies. Rodriguez-Fornells et al. (2005) has shown that there was an
increased negativity when the names of the pictures in a picture naming paradigm do not
match in both languages of a bilingual speaker. According to Misra et al. (2012) there was
an increased negativity in pictures that are named in the first language following naming
in the second language. In code-switching comprehension studies, an increased negativity
was also found for the sentences that contained a code switch (Moreno, Federmeier &
Kutas, 2002). So, when there is any kind of mismatch or a possibility of more than one
solution (“production”), then I will expect an increased negativity. The participant will be
asked to finish a sentence by describing a color (the adjective) and a picture (the noun).
The order in which the picture are presented match the orders described above in the MLF+
and MLF- conditions. In the MLF- conditions the participants are presented with two
possibilities, namely the adjective-noun order or the noun-adjective order. One of these
possibilities is what they expect after recognizing the language of the lead-in sentence
(which represents the matrix language) and the other order is given by them in the way the
pictures are presented (left-right). This leads to a mismatch between the grammars of the
two languages and consequently will elicit a more negative ERP signal.
With regard to the difference in neural acitivity between the different code-switches
patterns, I expect to find the same pattern of results as in the naming latency analysis. The
30
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
conditions with a switched noun will elicit a more positive ERP signal than conditions with
a switched adjective, because nouns are switched more frequently (Wentz, 1977; Timm,
1975) and can thus be processed more easily. No difference is expected between the neural
activity of a Dutch noun insertion and the neural activity of a Papiamento noun insertion.
Q3. Do the results from the naming latency analysis and the results from the
electrophysiological data analysis point in the same direction?
H3. Yes, I expect the results of the naming latency analysis to point in the same direction as the
results of the electrophysiological data analysis. The results found for Welsh-English codeswitching in naturalistic corpus data and elicited data (Parafita Couto et al., 2015) also
pointed in the same direction as the results from the electrophysiological data (Parafita
Couto et al., submitted), both yielded support for the MLF. Even though, I am looking at
naming latency data, the code-switching patterns used in this experiments are patterns that
were found in Dutch-Papiamento naturalistic corpus data.
The methodology of the experiment will now be explained.
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
31
5. Methodology
5.1. Participants
Eighteen bilingual Dutch-Papiamento speakers participated in this study, eleven females and
seven males. The participants were between 19 and 67 years old (mean age: 33.4; SD: 16.39).
Fifteen of the participants had learned Papiamento before the age of 2, one participant had
learned Papiamento before the age of 4 and two participants had learned Papiamento in
elementary school. Seven of the participants had learned Dutch before the age of 2, two
participants had learned Dutch before the age of 4, eight participants had learned Dutch in
elementary school and one had learned Dutch in secondary school. The highest level of
education reported ranged from high school to a Master’s degree. The participants originated
from all three Leeward islands of the Dutch Antilles, namely eight participants identified
themselves as “Curaçaoan”, one identified him/herself as “Bonairean”, three identified
themselves as “Aruban”, one identified him/herself as “Antillean”, one identified him/herself
as “Dutch” and four participants filled in something else (e.g. “Caribbean”, “Combination
Aruban/Dutch”, “World-citizen”). None of the participants had a reading or speaking disability.
Participants did not receive a reward and participated on a voluntary basis.
Participants rated their proficiency in both languages on a scale from 1 (knows a few words
and expressions) to 4 (confidently able to express oneself in complex conversations) (for the
procedure see section 5.3.). Results of the self-rated proficiency are shown in Table 2. Thirteen
participants (72.2%) feel confident expressing themselves in complex conversations in
Papiamento and twelve participants (66.7%) feel confident expressing themselves in complex
conversations in Dutch.
Table 2. Participants’ (N=18) self-rated proficiency of Papiamento and Dutch
Papiamento
Dutch
Scale
N
%
N
%
1 – Knows of a few words and expressions
0
0
0
0
2 – Able to express oneself in a basic conversation
2
11.1
1
5.5
3 – Able to express oneself in more complex conversations
3
16.7
5
27.8
4 – Confidently able to express oneself in complex conversations
13
72.2
12
66.7
Total
18
100
18
100
32
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
5.2. Material
The matrix language was reflected by the language of the lead-in sentence. The order of
adjective and noun can match the grammar of the matrix language (MLF+ conditions) or it can
mismatch the grammar of the matrix language (MLF- conditions). In the case of the MLF+
conditions, a Dutch lead-in sentence was followed by a (Dutch/Papiamento) adjective and then
a (Papiamento/Dutch) noun; a Papiamento lead-in sentence was followed by a
(Dutch/Papiamento) noun and then a (Papiamento/Dutch) adjective. In the case of the MLFconditions, a Dutch lead-in sentence was followed by a (Dutch/Papiamento) noun and then a
(Dutch/Papiamento) adjective; a Papiamento lead-in sentence was followed by a
(Dutch/Papiamento) adjective and then a (Dutch/Papiamento) noun.
To elicit the language switch between the adjective and the noun, a cue was used to tell the
participants where to switch. A round frame indicated that the picture or color patch– either
representing the adjective or the noun – needed to be produced in Papiamento and a square
frame indicates that the picture or color patch needed to be produced in Dutch.
In total there are eight conditions (2 predictions x 2 languages x 2 frame positions), as
shown in Table 3.
Table 3. The experimental conditions.
Place on the screen
Condition
Lead-in
sentence
Left
Frame
Right
Frame
1
MLF +
Dutch
Adjective
DUTCH (square)
Noun
PAP (round)
2
MLF +
Dutch
Adjective
PAP (round)
Noun
DUTCH (square)
3
MLF -
Dutch
Noun
PAP (round)
Adjective
DUTCH (square)
4
MLF -
Dutch
Noun
DUTCH (round)
Adjective
PAP (round)
5
MLF +
Papiamento
Noun
PAP (round)
Adjective
DUTCH (square)
6
MLF +
Papiamento
Noun
DUTCH (square)
Adjective
PAP (round)
7
MLF -
Papiamento
Adjective
DUTCH (square)
Noun
PAP (round)
8
MLF -
Papiamento
Adjective
PAP (round)
Noun
DUTCH (square)
Twenty pictures were selected from the Max Planck Institute for Psycholinguistics
database and consisted of simple black and white line drawings (Appendix A). Four color
patches were used to represent the adjectives. These color patches were created with the
computer programme Paint. The colors red, green, white and grey were used (Appendix A). All
of the picture and color names were non-cognates in Dutch and Papiamento. The Dutch and
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
33
Papiamento picture and color names were matched for number of phonemes and not for
frequency, because lists of frequency do not exist for Papiamento. Word frequency (for Dutch)
and length information is presented in Table 4. Each picture appeared once per condition; each
color appeared five times per condition. Each picture had its own lead-in sentence.
The lead-in sentence consisted of a subject phrase, a transitive verb and an indefinite article,
for example “[De buurman] [ziet] [een] […]” (“[the neighbour] [sees] [a] […]”). For each
condition, half of the lead-in sentences had a subject phrase that consisted of one word and the
other half had a subject phrase that consisted of two words. This division was made in order to
create some diversity in the sentences. The transitive verbs14 zien/mira (‘to see’), kopen/kumpra
(‘to buy’), verkopen/bènde (‘to sell’), tekenen/tek (‘to draw’) and stelen/hòrta (‘to steal’) were
used. Each verb appeared four times per condition. For the complete overview of the Dutch and
Papiamento lead-in sentences see Appendix B. All the words and sentences were checked by a
native speaker of Papiamento.
Table 4. Stimuli characteristics
Names in Dutch
Names in Papiamento
Frequency (per 1
Number of
million words)
phonemes
Mean
SD
Mean
SD
52.59
39.70
3.75
0.897
-
-
4.96
1.301
Note. Frequencies were derived from the SUBTLEX-NL corpus (Keuleers, Brysbaert & New, 2010)
5.3. Procedure
The experiment took place in the EEG lab at the University of Leiden. After providing informed
consent, the participants filled in an online ethno-linguistic background questionnaire via
Qualtrics (web-based survey software15). They could choose the language of the questionnaire,
there was one in Dutch (Appendix E) and one in Papiamento (Appendix F).
After filling in the background questionnaire, the participants were fitted with an electrode
cap and seated in an armchair. The participants were tested individually in a soundproof room.
The armchair was in front of a computer screen at a viewing distance of approximately 100 cm.
The microphone was located under the computer screen. Before the experiment began,
14
15
The verb in Dutch / the verb in Papiamento
https://uleidenss.eu.qualtrics.com
34
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
participants were instructed to minimize movement and to minimize blinking of the eyes, which
would disturb the EEG signal.
The participants first got acquainted with the pictures through a learning phase. They saw
all twenty pictures and four color patches one by one for 2,000 ms, with their Dutch or
Papiamento name below. After showing all the pictures, the participants saw one picture at a
time and they had to say the corresponding Dutch or Papiamento name out loud. They received
feedback on their responses. Half of the participants started the learning phase with the Dutch
names and the other half started with the Papiamento names.
After the learning phase, there was a practice phase. There were eight practice sentences,
one sentence of each condition. If the participant did not switch on all the practice trials, he or
she had to do the practice block again, until all the responses were correct according to the
conditions. After the practice block, the real experiment began. Each trial consisted of a 1,000
ms fixation cross, a 500 ms blank screen interval, a 500 ms subject-constituent, a 500 ms verbconstituent, a 500 ms determiner-constituent and, at last, a screen with a color and a picture.
The participants were instructed to finish the sentence by describing the color and the picture.
The screen with the color and the picture was shown until the participant responded (through a
voice key) or, in case of no response, for 5,000 ms. After the voice-response of the participant
there was an inter-trial-interval of 2,000 ms. The inter-trial-interval was used to code the voiceresponse of the participant. The experimenter pressed ‘0’ if there was no production, “1” if the
participant’s production was completely Dutch, “2” if the participant’s production was
completely Papiamento and pressed nothing when the production was a correct code switch.
This way the correct trials - according the requested condition - can be easily extracted for
analysis.
There were eight blocks of twenty trials. After each block there was a break, in which the
participants could relax and blink their eyes. By pressing a button the participant could continue
the experiment. The experiment took about an hour and a half.
5.4. EEG-recording and data analysis
The EEG-signal was recorded with a computer using ActiView software (BioSemi).
Continuous EEG was recorded from 38 electrodes, each referred to the TMS electrode. Thirtytwo electrodes (AF3, AF4, C3, C4, Cz, CP1, CP2, CP5, CP6, F3, F4, F7, F8, Fz, Fp1, Fp2,
FC1, FC2, FC5, FC6, O1, O2, Oz, P3, P4, P6, P7, Pz, PO3, PO4, T7, T8) were placed in an
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
35
electrode cap. Six additional electrodes were placed on the participants’ head. Two bipolar
electrodes were placed next to the left and the right eye to register horizontal eye movement,
two bipolar electrodes were placed above and beneath the left eye to register vertical eye
movement and two other electrodes were placed on the participants’ left and right mastoid as
offline reference channels. The EEG was continuously recorded and digitized at 512 Hz. The
EEG-signals were analysed with the computer program BrainVision Analyzer.
First the EEG was segmented into 7,000 ms epochs prior to the marker ‘93’, which was the
marker for the correct code switch trials. This way only the correct code switched trials were
segmented and the incorrect trials (indicated by the marker ‘90’, ‘91’or ‘92’, see section 5.3.)
were excluded from the analysis. The 7,000 ms time limit was chosen, because the marker for
the different conditions needed to be in that segment as well. Therefore the maximum time limit
for the target presentation (5,000 ms) and the time the experimenter needed to code the
responses (2,000 ms) needed to be taken into account. Second, the EEG signal was rereferenced to the average of the left and right mastoid. Thirdly, the EEG was processed through
a high pass filter with a cut-off frequency of 0.05 Hz (24 dB setting). The contribution of ocular
activity was corrected for and other artefacts were rejected at 25µV. Before a second
segmentation the EEG was passed through a low-pass filter with a cut-off frequency of 10 Hz
(24 dB setting). Higher low-pass filters resulted in too much alpha in the EEG signal. Then the
EEG was segmented into 1,000 ms long epochs starting from the stimulus onset. The 1,000 ms
long epochs were averaged in reference to the 200 ms pre-stimulus baseline. In total, ERP
analyses were based on an average of 12 segments per condition (condition 1: 10 segments,
condition 2: 12 segments, condition 3: 12 segments, condition 4: 12 segments, condition 5: 12
segments, condition 6: 13 segments, condition 7: 12 segments, condition 8: 13 segments). A
minimum of 10 segments was maintained, so at least half the trials could be analysed.
The ERPs from the MLF+ conditions and the MLF- conditions were compared at each
electrode by running 2-tailed paired t-tests at every sampling point (20 ms) starting from target
presentation (0 ms) until 1,000 ms after target presentation to identify the latency at which the
ERPs started to diverge significantly from one another. The same onset latency analysis was
used to compare the ERPs from the conditions that contain the same matrix language, but a
different word order.
Electrodes were clustered in nine groups as follows: left frontal (Fp1, AF3, F7, F3, FC5);
fronto-central (Fz, FC1, FC2, Cz); right frontal (Fp2, AF4, F8, F4, FC6); left central (T7, C3,
36
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
CP5); centro-parietal (CP1, CP2, Pz); right central (C4, T8, CP6); left parietal (P7, P3, PO3);
occipital (O1, Oz, O2) and right parietal (P4, P8, PO4).
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
37
6. Results
Two participants were excluded from the analysis, due to excess (ocular) artefacts which
resulted in a lack of usable segments. Therefore the analysis was based on sixteen participants.
First the results of the ethno-linguistic background questionnaire will be discussed, then the
behavioural data and at last the electrophysiological data.
6.1. Background questionnaire
Thirteen participants had filled in the background questionnaire in Dutch (Appendix E) and
three participants had filled in the questionnaire in Papiamento (Appendix F). For the remainder
of the questions, there will be no differentiation between answering in Dutch or in Papiamento.
The results of all the participants will be grouped together. The background questionnaire
consisted of twenty questions in total. The results of the questions concerning the participants’
age, education, age of acquisition and language proficiency are already incorporated in the
section about the participants (section 5.1.). The results of the remaining questions will be
discussed in this section.
The participants had to judge the two languages on five characteristics on a five-point scale
per characteristic. All five characteristics consisted of two opposite terms (e.g. old fashionmodern). The negatively loaded term was always on the left side of the scale and the positively
loaded term was always in the right side of the scale. When a participant judged a characteristic
exactly in the middle of the five-point scale, this was seen as a “neutral opinion”. Tables 5 and
6 show the results for the Dutch and Papiamento language respectively.
The majority of the participants had a neutral opinion about whether the Dutch language is
beautiful (N=9), inspiring (N=8), influential (N=7) and friendly (N=6). In the case of
Papiamento, the majority had a neutral opinion about whether the language is beautiful (N=9),
inspiring (N=7) and influential (N=5). Seven participants also had a neutral opinion about
whether Dutch is useful, but seven participants also completely agreed Dutch is useful. Whereas
for Papiamento six participants had a neutral opinion about usefulness and only four
participants completely agreed Papiamento is useful. Five participants had a neutral opinion
about whether Dutch is modern, but six participants completely agreed Dutch is modern. For
Papiamento, six participants had a neutral opinion and only five participants completely agreed
Papiamento is modern. In the case of friendliness of the Papiamento language, five participants
38
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
had a neutral opinion, six participants somewhat agreed Papiamento is friendly and five
participants completely agree Papiamento is friendly.
Table 5. The participants’ opinion (N=16) on five characteristics of the Dutch language.
THE DUTCH LANGUAGE
Five-point scale
Ugly
0
1
9
6
0
Beautiful
Useless
0
0
7
2
7
Useful
Uninspiring
0
4
8
1
3
Inspiring
Non-influential
0
2
7
4
3
Influential
Unfriendly
1
1
6
4
4
Friendly
Old fashion
0
0
5
5
6
Modern
Table 6. The participants’ opinion (N=16) on five characteristics of the Papiamento language.
THE PAPIAMENTO LANGUAGE
Five-point scale
Ugly
0
0
9
3
4
Beautiful
Useless
0
1
6
5
4
Useful
Uninspiring
0
1
7
4
4
Inspiring
Non-influential
1
5
5
1
4
Influential
Unfriendly
0
0
5
6
5
Friendly
Old fashion
0
1
6
4
5
Modern
Next, the participants were presented with two statements, on which they had to rate their
agreement on a five-point scale ranging from ‘strongly disagree’ to ‘strongly agree’. The first
statement is about their attitude towards code-switching; the second statement is about their
own production of code switches. Figures 1 and 2 show the results of the ratings.
Six participants (37.5%) had a negative attitude towards code-switching, four participants
(25%) had a neutral opinion and six participants (37.5%) had a positive attitude towards codeswitching. Nine participants (56.25%) claimed to switch in every day conversations, three
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
39
participants (18.75%) had a neutral opinion and four participants (25%) claimed to keep the
languages separate in every day conversations.
"People have to avoid using Papiamento and Dutch in the same conversation"
From left to right
Strongly disagree
1
5
4
3
Disagree
3
Neutral
Agree
0%
20%
40%
60%
80%
100%
Strongly agree
Figure 1. The participants’ opinion (N=16) on the statement “People should avoid using Papiamento
and Dutch in the same conversation”.
"In every day conversations I keep the Papiamento and the Dutch language
separate"
From left to right
Strongly disagree
Disagree
4
5
3
2
2
Neutral
Agree
0%
20%
40%
60%
80%
100%
Strongly agree
Figure 2. The participants’ opinion (N=16) on the statement “In everyday conversations I keep the
Papiamento and the Dutch language separate”.
The participants also had to write down which language(s) they speak with their mother,
father and caretaker (if applicable). Three participants indicated to speak solely Dutch with their
father and only one participant indicated to speak solely Dutch with his/her mother. Seven
participants indicated to speak solely Papiamento with their father and seven participants
indicated to speak solely Papiamento with their mother. Two participants indicated to speak
both languages with their father and two participants indicated to speak both languages with
their mother. Figure 3 summarizes these results.
40
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
8
7
Nr. of participants
6
5
4
Mother
3
Father
2
Caretaker
1
0
Dutch
Papiamento
Dutch and
Papiamento
Other
N/A
Language(s)
Figure 3. The language(s) spoken by the participants (N=16) with their mother, father and caretaker (if
applicable).
In the last question participants had to write down five names of people (or their relation)
with whom they converse the most on an everyday basis and then indicate which language they
use: Dutch, Papiamento or both. Most participants indicated to speak Dutch (56%), Papiamento
(38%) or both languages (57%) with their friends. Besides their friends, Dutch was most often
spoken with colleagues (18%), Papiamento was most spoken with their partner (19%), parents
(19%) or siblings (19%), and both languages were most spoken with their parents (19%). The
results were illustrated separately for Dutch, Papiamento and both Dutch/Papiamento in Figure
4.
Employee
4%
Dutch
Papiamento
Both
Parents
7%
Partner
5%
Partner
19%
Child
11%
Colleagues
18%
Friends
38%
Friends
56%
Relatives
4%
Parents
19%
Parents
19%
Siblings
19%
Friends
57%
Relatives
5%
Siblings
14%
Relatives
5%
Figure 4. Three circle diagrams for the relation between the conversational partners and the languages used. The
most left diagram is about the use of the Dutch language, the middle diagram is about the use of the Papiamento
language and the most right diagram is about the use of both Dutch and Papiamento.
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
41
6.2. Behavioural results
Both the behavioural data and the electrophysiological data has been split up into two analyses.
The first analysis deals with the predictions of the MLF; the second analysis deals with the
different types of code-switches. For all analysis a significance level of α = .05 was used.
6.2.1. Analysis of MLF predictions
The main goal of this study was to test the predictions of the MLF. In order to do so, I need to
compare conditions that match the predictions of the MLF (MLF+) with conditions that contain
a violation of the MLF (MLF-). I divided the eight experimental conditions into four pairs of
one MLF+ condition and one MLF- condition each. The two conditions of each pair contain
the same code-switching pattern, but in one condition, the MLF predicts the code-switching
pattern to be acceptable and in the other condition, the code-switching pattern will not be
acceptable according to the MLF. The analyses were based on the following pairs:
 Condition 1 versus condition 7
[MLF+ / ML=Dutch / Adj Dutch-Noun Pap] versus [MLF- / ML=Pap / Adj Dutch-Noun Pap]
 Condition 2 versus condition 8
[MLF+ / ML=Dutch / Adj Pap-Noun Dutch] versus [MLF- / ML=Pap / Adj Pap-Noun Dutch]
 Condition 5 versus condition 3
[MLF+/ ML=Pap / Noun Pap-Adj Dutch] versus [MLF-/ ML=Dutch / Noun Pap-Adj Dutch]
 Condition 6 versus condition 4
[MLF+/ ML=Pap / Noun Dutch-Adj Pap] versus [MLF-/ ML=Pap / Noun Dutch- Adj Pap]
The naming latency analysis revealed faster average responses in the MLF+ conditions
than in the MLF- conditions. The average naming latency difference of 16 ms was not
significant, t(3) = -0.489, p = .658. A paired sample t-test showed a significant difference in
picture naming when the Papiamento adjective was on the left and the Dutch noun was on the
right. Participants were significantly faster in the MLF+ condition (ML=Dutch) than in the
MLF- condition (ML=Pap), t(319) = -3.061, p = .002. Paired sample t-tests for other orders of
adjective and noun did not show significant naming latency differences between the MLF+ and
MLF- conditions: Adj Dutch – Noun Pap: t(319) = 1.367, p = .173, Noun Pap – Adj Dutch:
t(319) = -0.665, p = .506, Noun Dutch – Adj Pap: t(319) = 0.455, p = .649. Table 7 summarizes
42
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
the mean naming latencies of the eight experimental conditions, the significant difference
mentioned above is indicated with an ‘a’ in superscript.
6.2.2. Analysis of code-switching patterns
The second analysis is a small sidestep from the analysis of the MLF predictions. Even though
the MLF does not make any predictions about the origin of the adjective and the noun, I think
an analysis of the code-switching patterns might provide useful information for our
understanding of code-switching. I wanted to study the code-switching pattern more closely to
see whether a switched adjective elicits different behavioural (and electrophysiological) results
than a switched noun. Also to see whether switching within a sentence with Dutch as the matrix
language entails different results than switching within a sentence with Papiamento as the
matrix language. The conditions in each comparison contain the same prediction of the MLF
(+ or -), the same matrix language (Dutch or Papiamento), the same word order (adjective-noun
or noun-adjective), but a different type of switch (adjective switch or noun switch). The analysis
was based on the following pairs:

Condition 1 versus condition 2
[MLF+/ ML=Dutch/ Adj Dutch-Noun Pap] versus [MLF+/ ML=Dutch/ Adj Pap-Noun Dutch]

Condition 3 versus condition 4
[MLF-/ ML=Dutch/ Noun Pap-Adj Dutch] versus [MLF-/ ML=Dutch/ Noun Dutch- Adj Pap]

Condition 5 versus condition 6
[MLF+/ ML=Pap/ Noun Pap-Adj Dutch] versus [MLF+/ ML=Pap/ Noun Dutch-Adj Pap]

Condition 7 versus condition 8
[MLF-/ ML=Pap/ Adj Dutch-Noun Pap] versus [MLF-/ ML=Pap/ Adj Pap-Noun Dutch]
Results of a paired t-test of the naming latencies within the same matrix language paradigm
– but keeping MLF+ and MLF- conditions apart – revealed that participants responded
significantly faster in the MLF- condition with a Papiamento matrix language when there was
a Dutch adjective inserted than when a Dutch noun was inserted, t(319) = -4.038, p = < .001.
Other comparisons were not significant: MLF + / ML=Dutch: t(319) = 0.771, p = .441, MLF +
/ ML=Pap: t(319) = -0.819, p = .413, MLF- / ML=Dutch: t(319) = 0.297, p = .767. The
significant difference is highlighted with a ‘b’ in superscript in Table 7.
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
43
Table 7. Mean naming latencies (ms) in the 8 experimental conditions, divided into MLF+ and MLF- conditions.
Significant differences are indicated with superscript. ML = Matrix Language.
MLF –
MLF +
Adj Dutch – Noun Pap
1155 (649)
[1; ML = Dutch]
1104 (604)b
[7; ML = Pap]
Adj Pap – Noun Dutch
1130 (575)a
[2; ML = Dutch]
1231 (678)a,b
[8; ML = Pap]
Noun Pap – Adj Dutch
1174 (653)
[5; ML = Pap]
1203 (688)
[3; ML = Dutch]
Noun Dutch – Adj Pap
1207 (628)
[6; ML = Pap]
1192 (577)
[4; ML = Dutch]
Average
1166.5
Note. Standard deviations are in parentheses. a p < .001
1182.5
b
p < .001
A two-way ANOVA was conducted that examined the effect of matrix language and nounadjective order on naming latency. There was not a statistically significant interaction between
the effects of matrix language and noun-adjective order on naming latency, F(1,2556) = 0.413,
p= .520. No main effects for the matrix language (p = .716) nor the noun-adjective order (p =
.118) were found. Figure 5 shows the interaction between the matrix language and the nounadjective order. On the whole, participants reacted faster in conditions with an adjective-noun
word order than in conditions with a noun-adjective order. However, in the case of adjectivenoun word order, participants reacted faster when Dutch was the matrix language then when
Papiamento was the matrix language. In the case of noun-adjective word order, participants
reacted faster when Papiamento was the matrix language then when Dutch was the matrix
language.
44
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
Figure 5. A line graph of the interaction of matrix language and word order on naming latency.
6.3. Electrophysiological results
6.3.1. Analysis of MLF predictions
The conditions that were compared to each other to test the predictions of the MLF are repeated
below:
 Condition 1 versus condition 7
[MLF+ / ML=Dutch / Adj Dutch-Noun Pap] versus [MLF- / ML=Pap / Adj Dutch-Noun Pap]
 Condition 2 versus condition 8
[MLF+ / ML=Dutch / Adj Pap-Noun Dutch] versus [MLF- / ML=Pap / Adj Pap-Noun Dutch]
 Condition 5 versus condition 3
[MLF+/ ML=Pap / Noun Pap-Adj Dutch] versus [MLF-/ ML=Dutch / Noun Pap-Adj Dutch]
 Condition 6 versus condition 4
[MLF+/ ML=Pap / Noun Dutch-Adj Pap] versus [MLF-/ ML=Pap / Noun Dutch- Adj Pap]
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
45
The ERPs of the Grand Averages are shown in Figures 6 to 9 (twelve representative
electrodes, based on the depicted representative electrodes in Misra et al., 2012). All conditions
revealed a similar pattern, beginning with a small negative peak at approximately 50 ms after
target presentation, mostly visible in the occipital area. A second negative peak, maximal
between 80-140 ms was also observed. These negative peaks were followed by a positive peak,
maximal between 150-220 ms in the frontal and central area and between 220-300 ms in the
occipital area. This positive peak was the most prominent peak in the signal. A subsequent
positive peak was observed between 320-420 ms, though this peak was less clear than the first
positive peak. From 600 ms onwards, there seems to be a sustained positivity for the MLF+
condition in Figure 8 (condition 1 vs. 7) and 9 (condition 2 vs. 8), most noticeable in the frontal
and central areas. Whereas in Figure 10 (condition 5 vs. 3) and 11 (condition 6 vs. 4) a sustained
positivity for the MLF- condition was observed.
46
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
F3
Fz
F4
C3
Cz
C4
P3
Pz
P4
O1
Oz
O2
Amplitude (µV)
-8
time (ms)
-200
1000
10
Figure 6. Grand Average waveforms of twelve representative electrode sites for the MLF violation (dotted line)
and its correct counterpart (solid line) of condition 1 (MLF+ / ML=Dutch / Adj Dutch-Noun Pap) versus condition
7 (MLF- / ML=Pap / Adj Dutch-Noun Pap). Zero on the time axis marks the onset of the picture and color
presentation. Electrodes are arrayed from most anterior (top) to most posterior (bottom) and from left to right as
they were positioned on the scalp. Negativity is plotted up.
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
F3
Fz
F4
C3
Cz
C4
P3
Pz
P4
O1
Oz
O2
Amplitude (µV)
-8
47
time (ms)
-200
1000
10
Figure 7. Grand Average waveforms of twelve representative electrode sites for the MLF violation (dotted line)
and its correct counterpart (solid line) of condition 2 (MLF+ / ML=Dutch / Adj Pap-Noun Dutch) versus condition
8 (MLF- / ML=Pap / Adj Pap-Noun Dutch). Zero on the time axis marks the onset of the picture and color
presentation. Electrodes are arrayed from most anterior (top) to most posterior (bottom) and from left to right as
they were positioned on the scalp. Negativity is plotted up.
48
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
F3
Fz
F4
C3
Cz
C4
P3
Pz
P4
O1
Oz
O2
Amplitude (µV)
-8
time (ms)
-200
1000
10
Figure 8. Grand Average waveforms of twelve representative electrode sites for the MLF violation (dotted line)
and its correct counterpart (solid line) of condition 5 (MLF+/ ML=Pap / Noun Pap-Adj Dutch) versus condition
3 (MLF-/ ML=Dutch / Noun Pap-Adj Dutch). Zero on the time axis marks the onset of the picture and color
presentation. Electrodes are arrayed from most anterior (top) to most posterior (bottom) and from left to right as
they were positioned on the scalp. Negativity is plotted up.
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
F3
Fz
F4
C3
Cz
C4
P3
Pz
P4
O1
Oz
O2
Amplitude (µV)
-8
49
time (ms)
-200
1000
10
Figure 9. Grand Average waveforms of twelve representative electrode sites for the MLF violation (dotted line)
and its correct counterpart (solid line) of condition 6 (MLF+/ ML=Pap / Noun Dutch – Adj Pap) versus condition
4 (MLF-/ ML=Dutch / Noun Dutch – Adj Pap). Zero on the time axis marks the onset of the picture and color
presentation. Electrodes are arrayed from most anterior (top) to most posterior (bottom) and from left to right as
they were positioned on the scalp. Negativity is plotted up.
50
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
Onset latency analysis
Condition 1 versus condition 7 (adjective Dutch – noun Papiamento)
t-tests at each sampling rate indicated that the MLF- (ML=Pap) ERPs and the MLF+
(ML=Dutch) ERPs diverge significantly from one another in the occipital, parietal and right
central areas between 0-60 ms after target onset (MLF+/ ML=Dutch condition more positive).
The ERPs also diverge significantly from one another between 240-280 ms, in the occipital and
parietal areas (MLF-/ ML= Papiamento condition more positive).
Condition 2 versus condition 8 (adjective Papiamento – noun Dutch)
t-tests at each sampling rate has shown that the MLF- (ML=Pap) ERPs and the MLF+
(ML=Dutch) ERPs diverge significantly from one another between 300-940 ms in all skeletal
areas (MLF+/ ML=Dutch condition more positive).
Condition 5 versus condition 3 (noun Papiamento – adjective Dutch)
t-tests at each sampling rate indicated that the MLF- (ML=Dutch) ERPs and the MLF+
(ML=Pap) ERPs do not diverge significantly from one another (overall MLF-/ ML=Papiamento
condition more positive).
Condition 6 versus condition 4 (noun Dutch – adjective Papiamento)
t-tests at each sampling rate indicated that the MLF- (ML=Dutch) ERPs started to diverge
significantly from MLF+ (ML=Pap) ERPs 180-240 ms after target onset, in the left and right
frontal area, the left central area and the fronto-central area (MLF-/ ML=Dutch condition more
positive).
Time window analysis
On the basis of visual inspection of the Grand Averages, five time windows were selected for
statistical analyses. The first time window is from 150-220 ms after target presentation, the
second time window from 220-320 ms, the third time window is from 320-420 ms, the fourth
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
51
time window is 420-600 ms and the fifth window is 600-900 ms. The first and the second time
window mainly cover the peaks of the P300. The fifth time window covers the sustained
positivity.
Time window analyses were conducted to explore possible interactions between matrix
language, adjective-noun order and clustered electrodes. A 2 x 4 x 9 repeated measures
ANOVA was conducted with Matrix Language (Dutch vs. Papiamento), Order (Dutch Adj Pap Noun vs. Pap Adj – Dutch Noun vs. Pap Noun – Dutch Adj vs. Dutch Noun – Pap Adj)
and Electrode cluster (9 electrode clusters: see above) as independent variables. Mauchley’s
Test of Sphericity indicated that the assumption of sphericity had been violated16 in all time
windows (all p < .001). Therefore a Greenhouse-Geisser correction was used. Reported degrees
of freedom are uncorrected, but p-values are corrected.
150-220 time window
There was no main effect of Matrix Language, F(1,15) = 1.351, p = .263, Order, F(3,45) =
1.234, p = .292 or Electrode cluster, F(8,120) = 1.025, p = .358. There were no significant
interactions (p > .053).
220-320 time window
There was no main effect of Matrix Language, F(1,15) = 2.199, p = .159, or Order, F(3,45) =
1.613, p = .223. There was a main effect of Electrode cluster, F(8,120) = 14.973, p < .001. There
were no significant interactions (p > .097). The 150-220 and 220-320 time windows also
covered the peaks of the P300. Mean amplitudes were greater for the MLF- conditions than
MLF+ conditions, 4.18µV versus 3.21µV, t(3) = -1.391, p = .258.
320-420 time window
There was no main effect of Matrix Language, F(1,15) = 2.057, p = 0.172, or Order, F(3,45) =
3.502, p = .057. There was a significant main effect of Electrode cluster, F(8,120) = 7.956, p =
Mauchley’s Test of Sphericity tests the null-hypothesis that the variances of the differences are equal. If this is significant
we can reject the null-hypothesis and accept the alternative hypothesis that that the variances of the differences are not equal.
16
52
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
.001. There was a significant interaction between Order and Electrode cluster, F(24,360) =
2.456, p = .046. Other interactions were not significant (p > .088).
420-600 time window
There was no main effect of Matrix Language, F(1,15) = 1.971, p = 0.181, or Order, F(3,45) =
2.092, p = .147. There was a significant main effect of Electrode cluster, F(8,120) = 5.073, p =
.011. There was a significant interaction between Matrix Language and Electrode cluster,
F(8,120) = 3.394, p = .042. Other interactions were not significant (p > .269).
600-900 time window
There was no main effect of Matrix Language, F(1,15) = 3.297, p = .089, or Order, F(3,45) =
0.754, p = .461. There was a significant main effect of Electrode cluster, F(8,120) = 11.875, p
< .001. There was a significant interaction between Matrix Language and Electrode cluster,
F(8,120) = 4.259, p = .015. And there was a significant interaction between Order and Electrode
cluster, F(24,360) = 3.560, p = .007. Other interactions were not significant (p > .452).
Interim conclusion
The results from the onset latency analysis and the time window analysis do not seem to provide
conclusive support for the predictions of the MLF. The MLF only looks at the syntactic
structures of both languages and does not take any extra-linguistic variables into account.
However, an explanation for these inconclusive results can be sought in the way this study was
designed or in the group of participants that were used. An example of an extra-linguistic
variable, that might have had an influence, is the age of the group of participants. The
participants ranged from 19 to 67 years old and it could very well be that older people have
different opinions about code-switching or have different code-switching manners than younger
people. The participants of this study can be classified into ‘young participants’ (<30; N=12)
and ‘older participants’ (>30; N=4). If I look at the answers that were given on the two
statements about code-switching, I see that three out of the six participants that said that people
have to avoid using Dutch and Papiamento in the same conversation were older than thirty and
three out of the four people that said they keep the language separate were over thirty. So, the
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
53
older participants had a more negative attitude towards code-switching and more often claimed
they keep the two languages separate in every day conversations. It is only possible to make
strong claims about the results when there is no influence of other (extra-linguistic) variables
and the group of participants is as homogenous as possible. In a second analysis, the four older
participants were excluded to see if a reduced age range would make a difference in the results.
Though, it might be the case that the smaller group (the four older participants) is the group that
shows different results than the results from the first onset latency and time window analysis,
but a group of four people is too small to do a statistical analysis on. Therefore, the second
analysis was based on the twelve younger participants. The Grand Average waveforms for this
second analysis can be found in Appendix E.
The waveforms illustrate a similar pattern than the waveforms of all sixteen participants. It
begins with a negative peak, maximal between 50-150 ms. This negative peak was followed by
a positive peak, maximal between 150-220 ms in the frontal and central area, and maximal
between 220-320 ms in the occipital area. After the positive peak, the waveform goes into a
negative direction until 450 ms and into a more positive direction from 450 ms until 1,000 ms
after target onset.
Repeated onset latency analysis
Condition 1 versus condition 7 (adjective Dutch – noun Papiamento)
t-tests at each sampling rate indicated that the MLF- (ML=Pap) ERPs do not divergence
significantly from the MLF+ (ML=Dutch) ERPs. There were three time windows in which only
one electrode showed a significant difference, namely the PO3 electrode between 40-60 ms (p
= .035) and the Fp1 electrode between 540-560 ms (p = .022) and 560-580 ms (p = .024).
Between 40-60 ms, the MLF-/ ML=Dutch condition was more positive, and between 540-580
ms, the MLF-/ ML= Papiamento condition was more positive.
54
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
Condition 2 versus condition 8 (adjective Papiamento – noun Dutch)
t-tests at each sampling rate indicated that the MLF- (ML=Pap) ERPs and the MLF+
(ML=Dutch) ERPs diverge significantly form one another between 300-900 ms in all skeletal
areas (MLF+/ ML=Dutch condition more positive).
Condition 5 versus condition 3 (noun Papiamento – adjective Dutch)
t-tests at each sampling rate has shown that the MLF- (ML=Dutch) ERPs and the MLF+
(ML=Pap) ERPs diverge significantly from one another between 0-20 ms, in the left frontal,
fronto-central, left parietal and centro-parietal areas (MLF+/ ML= Papiamento condition more
positive).
Condition 6 versus condition 4 (noun Dutch – adjective Papiamento)
t-tests at each sampling rate indicated that the MLF- (ML=Dutch) ERPs started to diverge
significantly from the MLF+ (Pap) ERPs between 180-240 ms after target onset, in the left and
right frontal area, the left and right central area, the fronto-centro area and the centro-parietal
area (MLF-/ ML=Dutch condition more positive).
Repeated time window analysis
The same five time windows and repeated measures ANOVA with a Greenhouse-Geisser
correction were used as the first analysis. Reported degrees of freedom are uncorrected, but pvalues are corrected.
150-220 time window
There was no main effect of Matrix Language, F(1,11) = 1.864, p = .199, or Order, F(3,33) =
2.153, p = .165 or Electrode cluster, F(8,88) = 0.609, p = .547. There was a significant
interaction between Matrix Language and Electrode cluster, F(8,88) = 3.331, p = .037. Other
interactions were not significant (p > .185).
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
55
220-320 time window
There was no main effect of Matrix Language, F(1,11) = 2.246, p = .162 or Order, F(3,33) =
3.064, p = .095. There was a main effect of Electrode cluster, F(8,88) = 16.336, p <.001. There
were no significant interactions (p > .148). The 150-220 and 220-320 time windows also
covered the peak(s) of the P300. Mean amplitudes were greater for the MLF- conditions than
MLF+ conditions, 4.30µV versus 3.07µV, t(3) = -0.961, p = .408.
320-420 time window
There was no main effect of Matrix Language, F(1,11) = 3.195, p = .101. There was a main
effect of Order, F(3,33) = 5.531, p = .01917 and a main effect of Electrode cluster, F(8,88) =
19.874, p < .001. There were no significant interactions (p > .061).
420-600 time window
There was no main effect of Matrix Language, F(1,11) = 3.255, p = .099, or Order, F(3,33) =
3.401, p = .057. There was a main effect of Electrode cluster, F(8,88) = 12.130, p <.001. There
were no significant interactions (p > .090).
600-900 time window
There was no main effect of Matrix Language, F(1,11) = 3.415, p = .092, or Order, F(3,33) =
1.421, p = .264. There was a main effect of Electrode cluster, F(8,88) = 6.651, p = .004. There
were no significant interactions. There were no significant interactions (p > .050).
Table 8 gives an overview of the results of the first and second onset latency analysis and
Table 9 gives an overview of the results of the first and second time window analysis. The
significant divergences in the occipital and parietal areas, in the condition of a Dutch adjective
followed by a Papiamento noun, were replaced by significant divergences of single electrodes.
17
A Bonferroni post-hoc test was performed, showing that there was a linear effect (p = .007) and a cubic effect (p = .047).
However, the numbers of the factor “Order” are merely representations of a certain word order (1 = Dutch Adj – Pap Noun,
2= Pap Adj – Dutch Noun, 3 = Pap Noun – Dutch Adj, 4 = Dutch Noun – Pap Adj) and thus cannot be perceived as
something linear or cubic.
56
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
The condition, in which a Papiamento noun is followed by a Dutch adjective, illustrated a
significant divergence in the second onset latency analysis, which did not emerge in the first
onset latency analysis.
Table 8. An overview of the results of the first and second onset latency analysis. Only the time windows
in which a significant (“sign.”) divergence was found are mentioned. Between brackets it says which
MLF condition had a more positive (“pos.”) waveform in that particular time window.
Conditions
First onset latency analysis
Second onset latency analysis
-
- Sign. divergence between 0-60 ms
(MLF+ more pos.)
Adj Dutch- Noun Pap
(only the PO3 / MLF+ more pos.)
-
- Sign. divergence between 240-280 ms
(MLF- more pos.)
Sign. divergence between 40-60 ms
Sign. divergence between 540-560 ms
(only the Fp1 / MLF- more pos.)
-
Sign. divergence between 560-580
(only the Fp1 / MLF- more pos.)
Adj Pap – Noun Dutch
Noun Pap –Adj Dutch
Noun Dutch – Adj Pap
- Sign. divergence between 300-940 ms
-
(MLF+ more pos.)
- No sign. divergence
(MLF+ more pos.)
-
(overall MLF- more pos.)
- Sign. divergence between 180-240 ms
Sign. divergence between 300-900 ms
Sign. divergence between 0-20 ms
(MLF+ more pos.)
-
(MLF- more pos.)
Sign. divergence between 180-240 ms
(MLF- more pos.)
Table 9. An overview of the results of the first and second time window analysis. ML= Matrix Language.
Time windows
First time window analysis
Second time window analysis
150-220 ms
-
Nothing is significant
-
Sign. interaction ML and Cluster
220-320 ms
-
Main effect of Cluster
-
Main effect of Cluster
-
Main effect of Cluster
-
Main effect of Order
-
Sign. interaction Order and Cluster
-
Main effect of Cluster
-
Main effect of Cluster
-
Sign. interaction ML and Cluster
-
Main effect of Cluster
-
Main effect of Cluster
-
Sign. interaction ML and Cluster
-
Main effect of Cluster
-
Sign. interaction Order an Cluster
320-420 ms
420-600 ms
600-900 ms
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
57
6.3.2. Analysis of code-switching patterns
The conditions that were compared to each other to analyse the code-switching patterns are
repeated below:

Condition 1 versus condition 2
[MLF+/ ML=Dutch/ Adj Dutch-Noun Pap] versus [MLF+/ ML=Dutch/ Adj Pap-Noun Dutch]

Condition 3 versus condition 4
[MLF-/ ML=Dutch/ Noun Pap-Adj Dutch] versus [MLF-/ ML=Dutch/ Noun Dutch- Adj Pap]

Condition 5 versus condition 6
[MLF+/ ML=Pap/ Noun Pap-Adj Dutch] versus [MLF+/ ML=Pap/ Noun Dutch-Adj Pap]

Condition 7 versus condition 8
[MLF-/ ML=Pap/ Adj Dutch-Noun Pap] versus [MLF-/ ML=Pap/ Adj Pap-Noun Dutch]
The ERPs of the Grand Averages are shown in Figures 10 to 13 (twelve representative
electrodes, based on the depicted representative electrodes in Misra et al., 2012).
58
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
F3
Fz
F4
C3
Cz
C4
P3
Pz
P4
O1
Oz
O2
Amplitude (µV)
-8
-200
time (ms)
1000
10
Figure 10. Grand Average waveforms of twelve representative electrode sites for the MLF+ / Matrix Language =
Dutch condition with a Papiamento adjective inserted (dotted line) and the Papiamento noun inserted (solid line).
Zero on the time axis marks the onset of the picture and color presentation. Electrodes are arrayed from most
anterior (top) to most posterior (bottom) and from left to right as they were positioned on the scalp. Negativity is
plotted up.
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
F3
Fz
F4
C3
Cz
C4
P3
Pz
P4
O1
Oz
O2
Amplitude (µV)
-8
-200
59
time (ms)
1000
10
Figure 11. Grand Average waveforms of twelve representative electrode sites for the MLF- / Matrix Language =
Dutch condition with a Papiamento adjective inserted (dotted line) and the Papiamento noun inserted (solid line).
Zero on the time axis marks the onset of the picture and color presentation. Electrodes are arrayed from most
anterior (top) to most posterior (bottom) and from left to right as they were positioned on the scalp. Negativity is
plotted up.
60
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
F3
Fz
F4
C3
Cz
C4
P3
Pz
P4
O1
Oz
O2
Amplitude (µV)
-8
-200
time (ms)
1000
10
Figure 12. Grand Average waveforms of twelve representative electrode sites for the MLF+/ Matrix Language =
Papiamento condition with Dutch adjective inserted (dotted line) and the Dutch noun inserted (solid line). Zero
on the time axis marks the onset of the picture and color presentation. Electrodes are arrayed from most anterior
(top) to most posterior (bottom) and from left to right as they were positioned on the scalp. Negativity is plotted
up.
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
F3
Fz
F4
C3
Cz
C4
P3
Pz
P4
O1
Oz
O2
Amplitude (µV)
-8
-200
61
time (ms)
1000
10
Figure 13. Grand Average waveforms of twelve representative electrode sites for the MLF-/ Matrix Language =
Papiamento condition with Dutch adjective inserted (dotted line) and the Dutch noun inserted (solid line). Zero
on the time axis marks the onset of the picture and color presentation. Electrodes are arrayed from most anterior
(top) to most posterior (bottom) and from left to right as they were positioned on the scalp. Negativity is plotted
62
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
Onset latency analysis
Condition 1 versus condition 2 (MLF+ / Matrix Language=Dutch)
t-tests at each sampling rate has shown that the Dutch adjective-Papiamento noun ERPs and the
Papiamento adjective-Dutch noun ERPs diverge significantly from one another between 0-40
ms, in the occipital, left and right parietal areas (Papiamento noun insertion more positive),
between 120-140 ms, in the right parietal and right central areas (Papiamento adjective insertion
more positive), between 240-280 ms, in the right parietal and right central area (Papiamento
adjective insertion more positive) and between 360-500 ms, in the occipital, parietal, right
central and right frontal areas (Papiamento adjective insertion more positive).
Condition 3 versus condition 4 (MLF- / Matrix Language=Dutch)
t-tests at each sampling rate indicated that the Dutch noun-Papiamento adjective ERPs do not
diverge significantly from the Papiamento noun-Dutch adjective ERPs (overall Papiamento
adjective insertion more positive). There were several time windows in which just one electrode
showed a significant divergence, namely CP5 between 100-120 ms (p = .047), Cz between 400420 (p = .044), Cz between 420-440 ms (p = .027), Cz between 440-460 ms (p = .040), Oz
between 480-500 ms (p = .048), Oz between 500-520 ms (p = .048) and CP5 between 600-620
ms (p = .038).
Condition 5 versus condition 6 (MLF+ / Matrix Language =Papiamento)
t-tests at each sampling rate indicated that the Papiamento noun-Dutch adjective ERPs and the
Dutch noun-Papiamento adjective ERPs do not diverge significantly from one another (overall
Dutch noun insertion more positive). The F7 electrode showed a significant divergence between
180-200 ms (p = .029) and between 200-220 ms (p = .047).
Condition 7 versus condition 8 (MLF- / Matrix Language =Papiamento)
t-tests at each sampling rate indicated that the Dutch adjective-Papiamento noun ERPs diverged
significantly from the Papiamento adjective-Dutch noun ERPs between 320-360 ms in the
frontal areas (Dutch adjective insertion more positive). The two ERPs also diverge significantly
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
63
from one another between 800-920 ms, in all skeletal areas (Dutch adjective insertion more
positive). In addition, the Fp1 electrode showed a divergence between 640-800 ms (p < .039).
No second age-related analysis was done for the code-switching pattern data, because
establishing the behavioural and electrophysiological differences among the different codeswitching patterns was not the main goal of this study. The main goal was to test the predictions
of the MLF and therefore the MLF data was analysed in more detail.
6.4. Summary of the results
From the behavioural data it is clear that the participants responded faster when the matrix
language was Dutch then when the matrix language was Papiamento. Within the Dutch matrix
language paradigm, participants responded faster to adjective-noun constructions than to nounadjective constructions; this was even the case in the Papiamento matrix language paradigm.
Participants responded significantly faster in MLF-/ ML=Dutch conditions with a switched
Papiamento adjective than in MLF-/ ML=Dutch conditions with a switched Papiamento noun.
Significant faster responses were also found for the Papiamento adjective- Dutch noun order in
the MLF+ condition (ML=Dutch) in comparison to the MLF- condition (ML=Papiamento).
In the onset latency and time window analysis with the electrophysiological data, there
were significant differences in various time windows and skeletal regions. Nonetheless, it
became visible that MLF+ conditions had a more positive waveform in the first 60 ms, which,
in most conditions, was followed by a more positive waveform for the MLF- conditions. A
positive peak was found, with a maximum between 150-220 ms in the frontal and central area
and a maximum between 220-320 ms in the occipital area. This was seen as a P300, with a
higher amplitude in the MLF- conditions.
A second onset latency and time window analysis was done to test the possible influence
of an extra-linguistic variable, namely age of participants, on the inconclusive results. Though,
the results of this second age-related analysis did not show major effects.
64
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M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
65
7. Discussion
Behavioural data
The first research question addressed a theoretical question and the behavioural data was used
to answer this question: “Is there a difference in naming latencies between conditions that
match the predictions of the MLF (MLF+) and conditions that violate the predictions of the
MLF (MLF-)? In addition to this question, is there a difference in naming latencies with regard
to the different types of code-switches (adjective versus noun / Dutch versus Papiamento)?”
The behavioural data showed that the average naming latencies were slower for the MLFconditions than for the MLF+ conditions. However, if I look at each comparison of conditions
individually, it is not always the case that participants responded slower in the MLF- condition.
Participants responded faster in the MLF+ conditions in the case of a Papiamento adjective
followed by a Dutch noun (ML=Dutch) and in the case of a Papiamento noun followed by a
Dutch adjective (ML=Pap). The results of these two conditions would confirm my hypothesis
about slower naming latencies for conditions that violate the predictions of the MLF.
Nonetheless, participants responded faster in the MLF- condition in the case of a Dutch
adjective followed by a Papiamento noun (ML=Pap) and in the case of a Dutch noun followed
by a Papiamento adjective (ML=Dutch). In these two conditions, the order of the adjective and
the noun does not fit into the morpho-syntactic frame provided by the matrix language, but it
seems to follow the order of the language of the adjective. That is more in line with the
predictions of the Minimalist Program (MP – Cantone & MacSwan, 2009). In both cases of
faster responses in the MLF+ and faster responses in the MLF- conditions, there is a condition
that has Dutch as a matrix language and a condition that has Papiamento as the matrix language.
Consequently, no claim about the relationship between naming latencies and the matrix
language can be made yet. A two-way ANOVA also showed no significant interaction between
the matrix language and adjective-noun order on naming latency. The behavioural data does
not seem to support the predictions of the MLF completely, because the naming latency analysis
shows inconsistent results.
A comparison of the naming latencies within the same matrix language paradigm showed
that participants responded faster when an adjective was inserted than when a noun was
inserted, regardless of matrix language or MLF predictions. This faster naming response was
even significant in the case of the MLF- condition with Papiamento as the matrix language.
This response time pattern suggests that adjectives that come from a different language than the
matrix language trigger faster responses than nouns that come from a different language than
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M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
the matrix language. The results of this code-switching pattern analysis do not confirm my
hypothesis about faster production in conditions with a noun insertion.
Electrophysiological data
The second research question addressed the same theoretical question as the first research
question, only now the electrophysiological data was used to answer this question: “Is there a
difference in neural activity between conditions that match the predictions of the MLF (MLF+)
and conditions that violate the predictions of the MLF (MLF-)? In addition to this question, is
there a difference in neural activity with regard to the different types of code-switches (adjective
versus noun / Dutch versus Papiamento? The grand average waveforms of all conditions show
a similar pattern. From visual inspection it is clear that the MLF- conditions show a more
positive waveform than the MLF+ conditions, except the pair with the Papiamento adjective
first and the Dutch noun second, in which the waveform for the MLF- condition (ML=Pap) is
more negative than the waveform of the MLF+ condition (ML=Dutch). The positive waveform
of the other three MLF- conditions was not the trend I expected.
Onset latency analysis revealed significant differences between the MLF+ waveforms and
the MLF- waveforms in various time windows and skeletal regions. Only the comparison
between condition 5 and condition 3 (Papiamento noun-Dutch adjective), did not show a
significant divergence between the waveforms of the MLF+ condition (ML=Pap) and the MLFcondition (ML=Dutch). Early ERP components reflect automatic processing, while late ERP
components reflect (more) deep processing. There were two comparisons of conditions that
showed an early effect, namely the comparison of condition 1 versus condition 7, Dutch
adjective followed by a Papiamento noun, and the comparison of condition 6 versus condition
4, a Dutch noun followed by a Papiamento adjective. In the comparison of condition 1 versus
condition 7, the MLF+ (ML=Dutch) waveform was more positive, while in the comparison of
condition 6 versus condition 4, the MLF- (ML=Dutch) waveform was more positive. This could
indicate that processing the order of a Dutch adjective followed by a Papiamento noun in a
sentence with Dutch matrix language (condition 1) and a Dutch noun followed by a Papiamento
adjective in a sentence with Dutch matrix language (condition 4) are the “easiest” out of all
language-order combinations. Looking at the first condition, encountering a Dutch adjective
first is not uncommon (Dutch adjectives always precede the noun) and subsequently
encountering a Papiamento noun is possible, because participants are expecting a noun after a
Dutch adjective. Looking at the fourth condition, when participants encounter a Dutch noun,
they are fine, because adjectives are optional, and when this noun is followed by a Papiamento
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
67
adjective, they are still fine, because in Papiamento adjectives are always post-nominal. Perhaps
those combinations can be processed more automatically, because the grammars of Dutch and
Papiamento allow for, respectively, pre-nominal and post-nominal adjectives. Whereas, a prenominal Papiamento adjective (condition 2 versus 8) or a post-nominal Dutch adjective
(condition 5 versus 3) could be more difficult to process, because these orders are not consistent
with the Dutch and Papiamento grammar.
However, the comparison of condition 1 versus 7 (Dutch adjective-Papiamento noun) may
confirm the predictions of the MLF, the other comparison of condition 6 versus 4 (Dutch nounPapiamento adjective) does not. The order of a Dutch noun followed by a Papiamento adjective
might be explainable, though it is not the order presupposed by the matrix language, which in
this case is Dutch. Instead, the order resembles the word order presupposed by the language of
the adjective and this effect would go with the predictions of the MP (Cantone & MacSwan,
2009). Now, similar to the behavioural data, there is a comparison of conditions that seems to
validate the predictions of the MLF and there is a comparison of conditions that might be
explained by the predictions of the MP.
Results of a second analysis, which only included the participants under thirty, were quite
similar to the first. The early effect in the comparison of condition 6 and condition 4 (Dutch
noun followed by a Papiamento adjective) remains, though the very early effect in the
comparison of condition 1 and condition 7 (Dutch adjective followed by a Papiamento noun) is
no longer visible. Surprisingly, the waveform of condition 5 (Papiamento noun followed by a
Dutch adjective with Papiamento as the matrix language) was significantly more positive than
the waveform of condition 3 (Papiamento noun followed by a Dutch adjective with Dutch as
the matrix language), between 0-20 ms. This result (more positivity for a Papiamento noun
followed by a Dutch adjective in a sentence with Papiamento as the matrix language) provides
support for the predictions of the MLF. While comparing the results of the first and second
onset latency analysis, it became clear that the waveforms of the MLF+ conditions seems to be
significantly more positive in very early time windows (between 0 and 60 ms). However, this
significant positive direction stops after maximally 60 ms. Significant divergences in later time
windows show a more positive waveform for the MLF- conditions. So, in very early time
windows the MLF+ conditions follow the expected pattern, but these time window might be
too early to draw any real conclusions from. To sum up, reducing the age variability does not
seem to have a major effect on the electrophysiological data. Data of more participants
(preferably in the same age range) is needed to draw any conclusions about the influence of an
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M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
extra-linguistic variable, like age of participants. The overall predictions of the MLF need
further research as well.
Apart from the MLF predictions, do the results show any differences regarding the different
code-switching patterns? The grand average waveforms of the code-switching pattern analysis
do not show one universal trend. Within the Dutch matrix language paradigm, the waveform of
a Papiamento adjective insertion is more positive than the waveform of a Papiamento noun
insertion, in both the MLF+ and MLF- condition. As for the Papiamento matrix language
paradigm, the waveform of a Dutch adjective insertion is more positive in the MLF- conditions,
while the waveform of a Dutch noun insertion is more positive in the MLF+ conditions. The
latter confirms my hypothesis about a more positive waveform for noun insertions. The
hypotheses about the code-switching patterns (slower naming latencies and a more negative
waveform for adjective insertions) were confirmed by one of the four comparisons of conditions
in the electrophysiological data analysis, while it was not confirmed at all in the naming latency
analysis.
Perhaps the differences in the results, between conditions with an adjective insertion and
conditions with a noun insertion, are a result of the design of the experiment. The stimuli
consisted of twenty pictures (nouns) and only four colors (adjectives). In the case of a color,
participants had to decide whether it was ‘red’, ‘green’, ‘white’ or ‘grey’ and then decide in
which language they have to produce it in. In the case of a picture, it could be a ‘house’, ‘tree’,
‘shoe’, ‘dog’, ‘dress’, et cetera, resulting in a lot more options for the participant. Besides, each
color appeared five times per condition, while the pictures appeared once per condition and so
participants encountered the colors more often than the pictures. The frequent encounters and
the lesser options could have led to a faster recognition. Conditions with an inserted adjective
could seem easier to process for the participants than condition with an inserted noun. This
could lead to a more positive waveform for conditions with an adjective insertion.
Bringing the data together
The third research question was “Do the results from the naming latency analysis and the
results from the electrophysiological data analysis point in the same direction?”. Table 10
gives an overview of the conditions and the results from the behavioural data analysis and the
electrophysiological data analysis.
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
69
Table 10. The results of the behavioural data analysis and the electrophysiological data analysis for each
experimental condition.  = support, x = no support.
Condition
MLF
ML
1
MLF+
Dutch
2
MLF+
3
Type of data
Word order
Behavioural data
EEG data
Adj Dutch – Noun Pap
x
x
Dutch
Adj Pap – Noun Dutch


MLF-
Dutch
Noun Pap – Adj Dutch

x
4
MLF-
Dutch
Noun Dutch – Adj Pap
x
x
5
MLF+
Papiamento
Noun Pap – Adj Dutch

x
6
MLF+
Papiamento
Noun Dutch – Adj Pap
x
x
7
MLF-
Papiamento
Adj Dutch – Noun Pap
x
x
8
MLF-
Papiamento
Adj Pap – Noun Dutch


The electrophysiological data does point in a similar direction as the behavioural data. The
condition in which a Papiamento adjective is followed by a Dutch noun is the only condition
that illustrated the expected pattern, namely slower naming latencies in the MLF- condition
(ML=Pap) and a more negative waveform for the MLF- condition (ML=Pap). The condition,
in which a Papiamento noun is followed by a Dutch adjective, also shows the expected pattern
in the behavioural data, but not in the electrophysiological data.
The elicited ERP components
Previous studies on bilingual production (although not specifically on code-switching
production) mostly reported on the P200, N200 and P300 ERP components. In the present study
it seems that there is a P300 component, which is maximal between 150-220 ms in the frontal
and central areas and between 220-320 ms in the occipital areas. The amplitude of the P300
was higher in the MLF- conditions than in the MLF+ conditions. The P300 is usually addressed
in experiments using an “oddball paradigm”, in which participants are presented with sequences
of repetitive stimuli that are infrequently interrupted by a deviant stimulus. Previous studies
have shown that a larger P300 is elicited by the events representing the low-probability category
(Donchin and Coles, 1988) and that the P300 also increases as the complexity of the stimuli
and task increases (Johnson, 1986). I expected the task to be most complex in the MLFconditions, because in these conditions the order of the adjective and the noun does not match
the order presupposed by the grammar of the matrix language. The task of naming the color
and the picture is then more difficult for the participants. The larger P300 peak found in the
MLF- conditions could be explained by the higher complexity of the MLF- conditions.
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M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
The amplitude of the P300 can be explained, but the question remains of why a P300
occurs? There is yet no clear understanding of how and why the brain produces a P300 and
several theories have been proposed in the literature (for an overview see Polich, 2012). One
suggested theory is the context-updating theory (Donchin 1981; Donchin & Coles, 1988), in
which the P300 component indexes brain activities underlying revision of the mental
representation induced by incoming stimuli. Attentional processes govern a change or an
“update” of the stimulus representation when a new stimulus is detected. It could be the case
that the participants consistently had to update their stimulus representations, because the
representations in the MLF- conditions are new to the participants, i.e. the stimuli participants
encounter are unlike the Dutch pre-nominal adjective representation and Papiamento postnominal adjective representation that are familiar to the brain. These representations need to be
changed or “updated” into something that fits the requirements of the MLF- condition.
Another proposed theory is the neural inhibition theory (Polich, 2007), where it has been
hypothesized that the P300 reflects neural inhibition of on-going activity to facilitate
transmission of task/stimulus information from frontal (P3a) to temporal-parietal (P3b)
locations. The obtained frontal peak could reflect the P3a cub-component and the obtained
occipital peak could reflect the P3b sub-component, although this peak is in the occipital area
rather than the parietal area. One of the premises of the MLF is that both languages of a bilingual
speaker are always “on” during code-switching. In the Dutch-Papiamento word order conflict,
participants have to inhibit activation from one grammar to retrieve information from the other
and vice versa. For example, activation of the Papiamento grammar needs to be inhibited in
order to retrieve the Dutch pre-nominal order, but then the activation of the Dutch grammar
needs to be inhibited, because the lexical adjective needs to be retrieved from the Papiamento
grammar. This will lead to a Papiamento pre-nominal adjective, an example of an MLFconstruction.
Apart from the apparent P300, there is a sustained positivity visible in the grand average
waveforms. In the comparisons between conditions 1 versus 7 (Dutch adjective-Papiamento
noun) and conditions 2 versus 8 (Papiamento adjective-Dutch noun), there is a sustained
positivity for the MLF+ conditions (ML=Dutch), while in the comparisons between conditions
5 versus 3 (Papiamento noun-Dutch adjective) and the conditions 6 versus 4 (Dutch nounPapiamento adjective) there is a sustained positivity for the MLF- conditions (ML=Dutch). The
late positive component could reflect deep processing and reanalysing of the stimuli (related to
the P600 component found in sentence comprehension studies that reflects syntactic reanalysis
– Kaan, Harris, Gibson & Holcomb, 2000; Frisch, Schlesewsky, Saddy & Alpermann, 2002).
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
71
Though, this does not explain the distinction between sustained positivity for two MLF+
conditions and sustained positivity for two MLF- conditions. All four conditions, that showed
a sustained positivity, have Dutch as the matrix language. As a result, it seems that conditions
with Dutch as the matrix language, MLF+ or MLF-, are the conditions that need the most deep
processing and re-analysing. However, the analysis of the behavioural data nor the analysis of
the electrophysiological data reveal a particular difficulty with sentences that have Dutch as the
matrix language. This late positive component does not confirm neither rejects the hypothesis
about the predictions of the MLF.
All in all, the apparent P300 seems to fit into the context-updating and the neural inhibition
theory. Nevertheless, more research is necessary to confirm the appearance of a P300
component in code-switching production and to explain the apparent late positive component.
Further research is also necessary to discover which methods are most suitable for studying
code-switching production. In this study I tried to incorporate a methodological innovation to
test the predictions of a theoretical model. A (re)new(ed) methodology comes with new
methodological challenges.
Methodological challenges
This study has shown that it is not easy to study code-switching production with EEG. First of
all, people from the Dutch Antilles have a hairstyle that is not most suitable for EEG recordings.
Some people have frizzy hair or braids and so the electrodes can’t always make contact to the
scalp. That leads to a less clean signal. That was one of the reasons why I had set the minimum
of segments to 10, because otherwise I could not have included a lot of participants. Secondly,
it was difficult to get one universal way of spelling the stimuli, because there is a variety of
Papiamento spoken in Aruba (Papiamento) and a variety spoken in Bonaire and Curaçao
(Papiamentu) and these varieties mainly differ in orthography. Nevertheless, the focus of this
study is on production and not on spelling and thus one spelling variety was chosen and a native
speaker of Papiamento had checked the spelling for me.
During the experiments, participants did not seem to mind the spelling, whether it was
correct or incorrect according to their variety. The type of words seem to mind more. Some
participants told me that when they speak (on a daily basis) they don’t use some of the
Papiamento words themselves, but would use the Dutch equivalent. For example, they would
say the Dutch word beer (“bear”) instead of the Papiamento word oso (“bear”). Even though
there was a learning phase at the beginning of the experiment, participants mentioned they
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M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
sometimes had more trouble remembering the Papiamento word than thinking about the order
in which they had to produce it. This study was not designed to be a memory task. It might be
the case that the word oso is a borrowing from Spanish and perhaps not all Papiamento speakers
use or know this borrowing. The Papiamento language has a lot of influences of Portuguese,
Spanish and Dutch and this could result in borrowings or words that are phonologically similar
(cognates). Especially the amount of Papiamento-Dutch cognates made it very difficult to find
proper Papiamento nouns and enough Papiamento adjectives that could be used for this study.
The most challenging part of this study was to work with the variety of the Papiamento
language. So, there is a fine line between enough stimuli (keeping frequency, number of
phonemes, cognate effect, et cetera in mind) and maintaining the goal of the experiment.
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
73
8. Conclusion
In this study I have tested the predictions of a theoretical model in an innovative way, namely
by using a psycholinguistic method. In the field of code-switching, there are two prominent
theoretical models with each their own approach to code-switching. An electrophysiological
method was used to test the predictions of one of these models, namely the Matrix Language
Framework. However, the results of this study do not provide any conclusive support for the
predictions of the Matrix Language Framework. The explanation for some results might be
found within the Minimalist Program, however, this study was not designed to test the
predictions of the Minimalist Program and so this is merely a suggestion. From this study it is
not clear whether the Dutch-Papiamento code switches are governed by an asymmetry between
two languages (as proposed by the Matrix Language Framework) or by the grammars of both
languages (as proposed by the Minimalist Program). A study in code-switching production thus
remains exploratory in this field and I can only pose suggestions for explanations and
suggestions for further research. Data of more participants and data from language pairs with a
similar conflict in code-switching would benefit the results. Nevertheless, this study was a first
step in trying to incorporate methods and models from different fields of expertise and this line
of study needs to be continued to get a better understanding of code-switching and the syntactic
and psycholinguistic models. Further research should continue to look at code-switching
production and thereby keep the methodological challenges (e.g. language variety, type of
participants, number of stimuli) in mind.
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M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
75
Acknowledgments
I want to thank my supervisor, M. Carmen Parafita Couto, for introducing me to this topic and
her incredible guidance throughout the process. Also a big thank you to everyone who helped
me set up the experiment, recruit participants and analyse the data. Last but not least, I want to
thank all the participants for their time and input, because without them this study would not
have been possible.
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M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
Appendices
Appendix A – The pictures (Dutch name / Papiamento name)
Practice pictures
Bed / Kama
Boek/ Buki
Huis / Kas
Lamp / Lampi
Paard Kabai
Riem / Faha
Schoen / SapatoVlieger / Fli
Aap / Makako
Beer / Oso
Boom / Palu
Broek / Karson
Brood / Pan
Fiets / Baiskel
Hoed / Sombré
Hond / Kachó
Jas / Mantel
Jurk / Bistí
Klok / Oloshi
Koffer / Falis
Experiment pictures
81
82
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
Mes / Kuchú
Mond / Boka
Sleutel / Yabi
Taart / Bolo
Tafel / Mesa
Tas / Monton
Vis / Piská
Vogel / Para
Grijs / Shinishi
Rood / Kòrá
Wit / Blanku
The color patches
Groen / Bèrdè
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
83
Appendix B – The experimental sentences
Lead-in sentence (Dutch)
MLF+ /Dutch/ Adj-N
MLF - /Dutch/ N-Adj
Adj Pap - N
Dutch (22)
Shinishi aap
N Dutch Adj Pap (33)
Aap shinishi
N Pap - Adj
Dutch (44)
Makako grijs
Subject
Verb
Determiner
1
De man
ziet
een
Adj Dutch - N
Pap (11)
Grijze makako
2
Wij
zien
een
Grijze kachó
Shisnihi hond
Hond shinishi
Kachó grijs
3
Mijn broer
ziet
een
Grijze para
Shinishi vogel
Vogel shinishi
Para grijs
4
Maaike
tekent
een
Grijs kuchú
Shinishi mes
Mes shinishi
Kuchú grijs
5
Kees
verkoopt
een
Grijze mesa
Shinishi tafel
Tafel shinishi
Mesa grijs
6
Mijn vriend
verkoopt
een
Groene karson
Bèrde broek
Broek bèrde
Karson groen
7
De jongen
steelt
een
Groene sombré
Bèrde hoed
Hoed bèrde
Sombré groen
8
De man
steelt
een
Groene monton
Bèrde tas
Tas bèrde
Monton groen
9
Zij
verkoopt
een
Groene mantel
Bèrde jas
Jas bèrde
Mantel groen
10
Het kind
tekent
een
Groen yabi
Bèrde sleutel
Sleutel bèrde
Yabi groen
11
Wij
kopen
een
Witte piská
Blanku vis
Vis blanku
Piská wit
12
De buurman
ziet
een
Witte oso
Blanku beer
Beer blanku
Oso wit
13
Peter
koopt
een
Wit pan
Blanku brood
Brood blanku
Pan wit
14
Joost
tekent
een
Witte palu
Blanku boom
Boom blanku
Palo wit
15
Mijn ouders
verkopen
een
Witte oloshi
Blanku klok
Klok blanku
Oloshi wit
16
Mijn opa
koopt
een
Rode bisikleta
Còrá fiets
Fiets còrá
Bisikleta rood
17
Mijn vriendin
steelt
een
Rode falis
Còrá koffer
Koffer còrá
Falis rood
18
Mijn zus
koopt
een
Rode bolo
Còrá taart
Taart còrá
Bolo rood
19
Zij
tekent
een
Rode boka
Còrá mond
Mond còrá
Boka rood
20
Lola
steelt
een
Rode bistí
Còrá jurk
Jurk còrá
Bistí rood
84
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
Lead-in sentence (Pap)
MLF + / Pap / N-Adj
MLF - / Pap / Adj-N
N Dutch Adj Pap (66)
Aap shinishi
Adj Dutch - N
Pap (77)
Grijze makako
Adj Pap - N
Dutch (88)
Shinishi aap
Subject
Verb
Determiner
1
E hòmber
ta mira
un
N Pap - Adj
Dutch (55)
Makako grijs
2
Nos
ta mira
un
Kachó grijs
Hond shinishi
Grijze kachó
Shinishi hond
3
Mi ruman
ta mira
un
Para grijs
Vogel shinishi
Grijze para
Shinishi vogel
4
Maaike
ta tek
un
Kuchú grijs
Mes shinishi
Grijze kuchú
Shinishi mes
5
Kees
ta kumpra
un
Mesa grijs
Tafel shinishi
Grijze mesa
Shinishi tafel
6
Mi amigu
ta bènde
un
Karson groen
Broek bèrde
Groene karson
Bèrde broek
7
E hòmber
ta hòrta
un
Sombré groen
Hoed bèrde
Groene sombré
Bèrde hoed
8
E hòmber
ta hòrta
un
Monton groen
Tas bèrde
Groene monton
Bèrde tas
9
E
ta bènde
un
Mantel groen
Jas bèrde
Groene mantel
Bèrde jas
10
E mucha
ta tek
un
Yabi groen
Sleutel bèrde
Groene yabi
Bèrde sleutel
11
Nos
ta tuma
un
Piská wit
Vis blanku
Witte piská
Blanku vis
12
E bisiña
ta bènde
un
Oso wit
Beer blanku
Witte oso
Blanku beer
13
Peter
ta kumpra
un
Pan wit
Brood blanku
Witte pan
Blanku brood
14
Joost
ta tek
un
Palo wit
Boom blanku
Witte palo
Blanku boom
15
Mi mayonan
ta bènde
un
Oloshi wit
Klok blanku
Witte oloshi
Blanku klok
16
Mi padú
ta bènde
un
Bisikleta rood
Fiets còrá
Rode bisikleta
Còrá fiets
17
Mi amiga
ta hòrta
un
Falis rood
Koffer còrá
Rode falis
Còrá koffer
18
Mi ruman
ta kumpra
un
Bolo rood
Taart còrá
Rode bolo
Còrá taart
19
E
ta tek
un
Boka rood
Mond còrá
Rode boka
Còrá mond
20
Lola
ta hòrta
un
Bistí rood
Jurk còrá
Rode bistí
Còrá jurk
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
85
Appendix C – The information form (in Dutch)
Leiden University Centre for Linguistics
Begeleider: Dr. M. Carmen Parafita Couto
Onderzoeker: Myrthe Wildeboer
Titel van het onderzoek: EEG-onderzoek naar tweetaligheid
Beste deelnemer,
We vragen u deel te nemen aan een onderzoek waarmee we hopen meer te weten te komen over
tweetaligheid. We doen dit door naar de activiteit van de hersenen te kijken tijdens het lezen en
spreken.
Inhoud van het onderzoek
Het experiment bestaat uit drie onderdelen: een leerblok, een oefenblok en het experiment. Tijdens het
leerblok verschijnt er één voor één een afbeelding met een naam in beeld. Aan u de taak om de juiste
naam bij de juiste afbeelding te onthouden. Nadat u alle afbeeldingen en bijbehorende namen eenmaal
gezien heeft, zult u de afbeeldingen zelf moeten gaan benoemen. Er komt elke keer één afbeelding
tegelijk in beeld. Aan u de taak om de juiste naam hardop te zeggen. U krijgt hier feedback, zodat u
kunt zien of u de juiste naam heeft genoemd of niet. Probeer meteen de naam te zeggen en geen “ehh”
of andere aarzelingen te gebruiken. Dit doet u één keer met Nederlandse namen en één keer met
Papiamento namen.
Tijdens het echte experiment zult u stukje voor stukje een deel van een zin te zien krijgen. Dit deel van
de zin hoeft u niet hardop voor te lezen of te benoemen, maar kunt u voor uzelf lezen. Daarna zult u
tegelijkertijd een afbeelding en een kleur te zien krijgen. Aan u de taak om de zin af te maken door de
zowel de afbeelding als de kleur hardop te benoemen. U moet beide afbeeldingen benoemen. Probeer
meteen de afbeeldingen te benoemen en geen “ehh” of andere aarzelingen te gebruiken.
LET OP: om zowel de kleur als de afbeelding staat een frame. Een vierkant frame betekent dat u die
afbeelding of kleur in het Nederlands moet benoemen. Een rond frame betekent dat u die afbeelding
of kleur in het Papiaments moet benoemen. Er zal altijd één vierkant frame zijn en één rond frame, u
wisselt hier dus van taal. Voordat het experiment begint, kunt u wennen aan de taak door middel van
een oefenblok.
Een voorbeeld:
De man
ziet
Dit gedeelte leest u in uw hoofd
een
Hier zegt u: SHINISHI VOGEL
86
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
Voordat we het experiment kunnen beginnen zullen we het EEG materiaal aansluiten. U krijgt hierbij
een cap op uw hoofd, waar 32 elektroden in worden geplaatst. Daarnaast komen er nog 6 elektroden op
uw gezicht: één op beide slapen, één boven het linkeroog, één onder het linkeroog en één achter elk oor.
Het is een veilige methode en u zult er geen schade aan overhouden. Het aansluiten van het materiaal
zal ongeveer een half uur duren. Na het experiment heeft u de mogelijkheid om uw haar te wassen.
We vragen u ook een vragenlijst in te vullen voor achtergrondinformatie. Deze informatie is puur en
alleen voor dit onderzoek bedoeld en zal niet aan derden worden verstrekt.
Het is belangrijk dat u het gehele experiment zo geconcentreerd mogelijk blijft. Probeer zo min mogelijk
te knipperen, dit kan namelijk storingen veroorzaken in het signaal. Gelieve ook zo stil mogelijk te
zitten. Alle geluiden en bewegingen (slikken, smakken, zuchten, tanden knarsen, etc) kunnen storingen
opleveren, waardoor het signaal moeilijker te analyseren wordt. Er zullen voldoende pauzes zijn waarin
u kunt ontspannen en even kunt knipperen, waardoor u daarna weer geconcentreerd verder kunt met het
experiment.
Vrijwilligheid van deelname
Deelname aan dit onderzoek is geheel vrijwillig en vrijblijvend. Dit betekent dat u te allen tijde,
zonder opgaaf van reden, kunt besluiten om uw deelname aan het onderzoek te beëindigen.
Vertrouwelijkheid van informatie
Alle informatie die in het kader van dit onderzoek wordt verzameld, wordt als strikt vertrouwelijk
behandeld. Alle gegevens worden in anonieme vorm verwerkt en bewaard. Er zal voor worden gezorgd
dat onbevoegden er geen inzage in krijgen en ook dat de gegevens niet tot personen zijn terug te leiden.
Dit onderzoek wordt gecoördineerd door Myrthe Wildeboer ([email protected]).
Indien u vragen heeft over dit onderzoek kunt u dat met haar bespreken.
Klachten
Indien u vindt dat u onjuist bent geïnformeerd over dit onderzoek, of klachten heeft over de uitvoering
of bejegening tijdens dit onderzoek, verdient het aanbeveling dit te bespreken met de onderzoeker of
met de coördinator van het onderzoek. Indien u dat niet wilt, of indien dat geen oplossing geeft, kunt u
ook een klacht indienen bij bestuur van het instituut LUCL. Onderaan vindt u de contactgegevens.
Toestemmingsverklaring
Voor deelname aan het onderzoek hebben wij vanzelfsprekend uw toestemming nodig. Als u bereid
bent om mee te doen, kunt u dit op het hier bijgevoegde toestemmingsformulier aangeven.
Contact informatie
Onderzoeker: Myrthe Wildeboer
E-mail: [email protected]
Leiden University Centre for Linguistics (LUCL):
Adres: P. N. van Eyckhof 3, NL-2311 BV, Leiden, Nederland
Telefoon: 071-5271662 (Gea Hakker, Instituut Manager)
E-mail: [email protected]
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
87
Appendix D – The consent form (in Dutch)
Leiden University Centre for Linguistics
Begeleider: Dr. M. Carmen Parafita Couto
Onderzoeker: Myrthe Wildeboer
Titel van het onderzoek: EEG-onderzoek naar tweetaligheid
Toestemmingsverklaring
Door dit formulier te ondertekenen geeft u te kennen de proefpersoneninformatie te hebben
gelezen en dat u deze heeft begrepen. Verder geeft u door dit formulier te ondertekenen aan
dat u akkoord gaat met de in het informatieformulier beschreven procedures.
U heeft het informatieformulier gelezen en begrepen en geef toestemming voor deelname aan
het onderzoek.
Datum: ………………………….
Plaats: …………………
Naam: ……………………………
Handtekening: ………..
88
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
Appendix E - The background questionnaire in Dutch
Vragenlijst
Deelnemer nr. .............
We zouden u erg dankbaar zijn als u ons de volgende achtergrond informatie wilt geven om
ons te helpen met ons onderzoek.
1. Bent u:
Man 
Vrouw ? 2. Leeftijd:……………….………
3. Wat is op dit moment uw beroep (of als u met pensioen bent of werkloos, wat was het
laatste beroep dat u hebt beoefend voordat u met pensioen bent gegaan of werkloos bent
geworden)?
............................................................................................................................
4. Geef alstublieft aan waar u voor langere perioden hebt gewoond:
v.b.:
Plaats: Willemstad, Curaçao
Data: 1982-1993
Plaats: Kralendijk, Bonaire
Data: 1993-1999
Plaats: Tilburg, Nederland
Data: 1999-2002 +
Plaats: Leiden, Nederland
Data: 2002-2005
Plaats: …………………………………………
Data: ……….…………………
Plaats: …………………………………………
Data: ……….…………………
Plaats: …………………………………………
Data: ……….…………………
Plaats: …………………………………………
Data: ……….…………………
Plaats: …………………………………………
Data: ……….…………………
Plaats: …………………………………………
Data: ……….…………………
5. Vind u uzelf voornamelijk…?
 Curaçaoënaar
 Bonaireaan
 Arubaan
 Antilliaans
 Nederlandse
 Anders (geef a.u.b. aan wat):……………………………
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
89
6. Hoe zou u Papiamento als taal op een schaal van 1 tot 5 rangschikken volgens de volgende
eigenschappen? Omcirkel één nummer in elke regel.
ouderwets
onvriendelijk
zonder invloed
niet inspirerend
nutteloos
lelijk
1
1
1
1
1
2
2
2
2
2
1
3
3
3
3
3
2
4
4
4
4
4
3
5
5
5
5
5
4
modern
vriendelijk
invloedrijk
inspirerend
bruikbaar
5
mooi
7. Hoe zou u Nederlands als taal op een schaal van 1 tot 5 rangschikken volgens de volgende
eigenschappen? Omcirkel één nummer in elke regel.
ouderwets
onvriendelijk
zonder invloed
niet inspirerend
nutteloos
lelijk
1
1
1
1
1
2
2
2
2
2
1
3
3
3
3
3
2
4
4
4
4
4
3
5
5
5
5
5
4
modern
vriendelijk
invloedrijk
inspirerend
bruikbaar
5
mooi
8. In hoeverre bent u het eens met de volgende stelling:
“In alledaagse gesprekken houd ik de talen Papiamento en Nederlands gescheiden.”
1
2
3
4
5
Geheel mee oneens
Oneens
Niet eens of oneens
Eens
Geheel mee eens
9. In hoeverre bent u het eens met de volgende stelling:
“Mensen moeten het vermijden om Papiamento en Nederlands met elkaar te mengen in
hetzelfde gesprek.”
1
2
3
4
5
Geheel mee oneens
Oneens
Niet eens of oneens
Eens
Geheel mee eens
10. Wat is uw hoogst genoten opleiding?
 Basisonderwijs
 MAVO/VMBO
 MBO
 HAVO
 VWO
 HBO
 Universitair – Bachelor
90
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
 Universitair – Master
 Geen
11. Vanaf wanneer kunt u Papiamento spreken?
 Vanaf dat ik 2 jaar of jonger was
 Vanaf dat ik 4 jaar of jonger was
 Vanaf de basisschool
 Vanaf de middelbare school
 Ik heb Papiamento leren spreken als volwassene
12. Vanaf wanneer kunt u Nederlands spreken?
 Vanaf dat ik 2 jaar of jonger was
 Vanaf dat ik 4 jaar of jonger was
 Vanaf de basisschool
 Vanaf de middelbare school
 Ik heb Nederlands leren spreken als volwassene
13. Op een schaal van 1 tot 4, hoe goed vind u dat u Papiamento kunt spreken?
 1 Ik ken alleen een paar woorden en uitdrukkingen
 2 Ik kan me met vertrouwen uiten in een basisgesprek
 3 Ik kan me met wat vertrouwen uiten in uitgebreide gesprekken
 4 Ik kan me met volle vertrouwen uiten in uitgebreide gesprekken
14. Op een schaal van 1 tot 4, hoe goed vind u dat u Nederlands kunt spreken?
 1 Ik ken alleen een paar woorden en uitdrukkingen
 2 Ik kan me met vertrouwen uiten in een basisgesprek
 3 Ik kan me met wat vertrouwen uiten in uitgebreide gesprekken
 4 Ik kan me met volle vertrouwen uiten in uitgebreide gesprekken
15. Welke taal (of talen) heeft uw moeder met u gesproken wanneer u aan het opgroeien was
(indien van toepassing)?
 Papiamento
 Nederlands
 Papiamento & Nederlands
 Anders (geef a.u.b. aan welke)……………………………
 Niet van toepassing
16. Welke taal (of talen) heeft uw vader met u gesproken wanneer u aan het opgroeien was
(indien van toepassing)?
 Papiamento
 Nederlands
 Papiamento & Nederlands
 Anders (geef a.u.b. aan welke)……………………………
 Niet van toepassing
17. Welke taal (of talen) heeft een andere voogd of verzorger met u gesproken wanneer u aan
het opgroeien was (indien van toepassing)?
 Papiamento
 Nederlands
 Papiamento & Nederlands
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
91
 Anders (geef a.u.b. aan welke)……………………………
 Niet van toepassing
18. In welke taal (of talen) kreeg u voornamelijk les op de basisschool?
 Papiamento
 Nederlands
 Papiamento & Nederlands
 Anders (geef a.u.b. aan welke)……………………………………
19. In welke taal (of talen) kreeg u voornamelijk les op de middelbare school?
 Papiamento
 Nederlands
 Papiamento & Nederlands
 Anders (geef a.u.b. aan welke)……………………………………
20. Maak hieronder een lijst van vijf mensen waarmee u het vaakst mee in uw alledaagse
leven spreekt, hetzij persoonlijk of aan de telefoon, bijvoorbeeld uw partner, uw kind, een
vriend(in), een collega etc. Noteer daarbij welke talen u het vaakst gebruikt tijdens een
gesprek met die persoon, zoals te zien in de voorbeeldtabel.
Naam van
persoon of
relatie
Taal meest gesproken met die persoon:
(plaats een vinkje in één vakje hieronder voor elke regel)
Papiamento
1. Jan
2. Moeder
3. Baas
4. Janneke
5. Zus
Nederlands

Zowel Papiamento
als Nederlands
Een andere
taal




Vul alstublieft onderstaand tabel in
Naam van persoon
of relatie
(gebruik fictieve
namen als u
wilt)
1.
2.
3.
4.
5.
Taal meest gesproken met die persoon:
(plaats een vinkje in één vakje hieronder voor elke regel)
Papiamento
Nederlands
Zowel Papiamento Een andere
als Nederlands
taal
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Appendix F – The background questionnaire in Papiamento
Kuestionario
Participante no.............
Nos lo ta hopi buenagradesidu si señor(a) lo por duna nos e siguiente information di señor(a)
su pasado pa yuda nos ku nos investigashon.
1. Shon ta:
Homber 
Muhé ?
2. Edat:……………….………
3. Kiko ta señor(a) su profeshon na e momentu aki (of si señor(a) ta ku penshon of si señor(a)
ta desempleá, kiko tabata e delaster profeshon ku señor(a) tabata tin prome ku señor(a) a
baha ku penshon of a bira desempleá)?
............................................................................................................................
4. Por fabor indiká na unda señor(a) a biba pa tempu significante:
v.b.:
Lugá: Willemstad, Kòrsou
Fecha: 1982-1993
Lugá: Kralendijk, Bonèiru
Fecha: 1993-1999
Lugá: Tilburg, Hulanda
Fecha: 1999-2002
Lugá: Leiden, Hulanda
Fecha: 2002-2005
Lugá: …………………………………………
Fecha: ……….…………………
Lugá: …………………………………………
Fecha: ……….…………………
Lugá: …………………………………………
Fecha: ……….…………………
Lugá: …………………………………………
Fecha: ……….…………………
Lugá: …………………………………………
Fecha: ……….…………………
Lugá: …………………………………………
Fecha: ……….…………………
5. Kon señor(a) ta sinti su mes prinsipalmente?
 Kurasoleño
 Bonerianu
 Rubiano
 Antiano
 Hulandes
 Otro (por fabor nombra kua)……………………………
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
6. Kon lo señor(a) pone Papiamento komo lenga riba un eskala di 1 te 5 sigun e siguiente
karakterístikanan? Sirkulá un number riba tur liña.
antikuá
desagradabel
sin influensha
sin inspirashon
inútil
mahos
1
1
1
1
1
1
2
2
2
2
2
2
3
3
3
3
3
3
4
4
4
4
4
4
5
5
5
5
5
5
modèrnu
agradabel
influyente
inspirá
utilisabel
bunita
7. Kon lo señor(a) pone hulandes komo lenga riba un eskala di 1 te 5 sigun e siguiente
karakterístikanan? Sirkulá un number riba tur liña.
antikuá
desagradabel
sin influensha
sin inspirashon
inútil
mahos
1
1
1
1
1
1
2
2
2
2
2
2
3
3
3
3
3
3
4
4
4
4
4
4
5
5
5
5
5
5
modèrnu
agradabel
influyente
inspirá
utilisabel
bunita
8. Den ki medida señor(a) ta di akuerdo ku e siguiente:
“Den kòmbersashon di tur dia mi ta tene e lenganan Papiamento i hulandes separá.”
 1 Mi no ta kompletamente di akuerdo
 2 Mi no ta di akuerdo
 3 Mi ta neutral
 4 Mi ta di akuerdo
 5 Mi ta kompletamente di akuerdo
9. Den ki medida señor(a) ta di akuerdo ku e siguiente:
“Hende mester evitá di usa Papiamento i hulandes den un kòmbersashon.”
 1 Mi no ta kompletamente di akuerdo
 2 Mi no ta di akuerdo
 3 Mi ta neutral
 4 Mi ta di akuerdo
 5 Mi ta kompletamente di akuerdo
10. Kua nivel di edukashon ta e nivel supremo ku señor(a) a gosa di dje?
 Enseñansa básiko
 MAVO/VMBO
 MBO
 HAVO
 VWO
 HBO
 Universidat – Bachelor
 Universidat – Master
 Niun
93
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M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
11. For di kua tempu señor(a) por papia Papiamento?
 For di mi tabata tin 2 aña of menos
 For di mi tabata tin 4 aña of menos
 For di enseñansa básiko
 For di skol sekundario
 Mi a siña papia Papiamento komo adulto
12. For di kua tempu señor(a) por papia hulandes?
 For di mi tabata tin 2 aña of menos
 For di mi tabata tin 4 aña of menos
 For di enseñansa básiko
 For di skol sekundario
 Mi a siña papia hulandes komo adulto
13. Kon bon señor(a) ta pensa señor(a) por papia Papiamento riba un eskala di 1 te 4?
 1 Mi konose un par di palabra ku ekspreshon so
 2 Mi por ekspresá mi mes ku konfiansa den un kòmbersashon básiko
 3 Mi por ekspresá mi mes ku un tiki konfiansa den un kòmbersashon amplio
 4 Mi por ekspresá mi mes ku hopi konfiansa den un kòmbersashon amplio
14. Kon bon señor(a) ta pensa señor(a) por papia hulandes riba un eskala di 1 te 4?
 1 Mi konose un par di palabra ku ekspreshon so
 2 Mi por ekspresá mi mes ku konfiansa den un kòmbersashon básiko
 3 Mi por ekspresá mi mes ku un tiki konfiansa den un kòmbersashon amplio
 4 Mi por ekspresá mi mes ku hopi konfiansa den un kòmbersashon amplio
15. Kua lenga(nan) señor(a) su mama tabata papia ku señor(a) ora señor(a) tabata kresiendo
(si ta aplikabel)?
 Papiamento
 Hulandes
 Papiamento & hulandes
 Otro (por fabor nombra kua)……………………………
 No ta aplikabel
16. Kua lenga(nan) señor(a) su tata tabata papia ku señor(a) ora señor(a) tabata kresiendo (si
ta aplikabel)?
 Papiamento
 Hulandes
 Papiamento & hulandes
 Otro (por fabor nombra kua)……………………………
 No ta aplikabel
17. Kua lenga(nan) señor(a) su vogt of kuidadó tabata papia ku señor(a) ora señor(a) tabata
kresiendo (si ta aplikabel)?
 Papiamento
 Hulandes
 Papiamento & hulandes
 Otro (por fabor nombra kua)……………………………
 No ta aplikabel
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
95
18. Na kua lenga(nan) señor(a) a haña les durante di señor(a) su enseñansa básiko?
 Papiamento
 Hulandes
 Papiamento & hulandes
 Otro (por fabor nombra kua)……………………………
19. Na kua lenga(nan) señor(a) a haña les durante di señor(a) su skol sekundario?
 Papiamento
 Hulandes
 Papiamento & hulandes
 Otro (por fabor nombra kua)……………………………
20. Traha un lista akibou di sinku hende ku señor(a) ta papia ku ne mas tantu den señor(a) su
bida di tur dia, sea personalmente of na telefòn, por ehèmpel señor(a) su partner, su yu,
un amigu/amiga, un kolega etc. Nota ku esei kua lenga(nan) señor(a) ta usa durante di un
kòmbersashon ku e persona ei, manera den e tabèl di ehèmpel.
Nomber di
persona of
relashon
Lengá mas papiá ku e persona ei:
(marka e den e vak pa tur persona of relashon)
Papiamento
1. Jan
2. Moeder
3. Baas
4. Janneke
5. Zus
Hulandes

Tantu
Papiamento
komo hulandes
Un otro
lenga




Por fabor yena e tabèl akibou
Nomber di persona
of relashon (usa
nomber fiktisio
si ta nesesario)
1.
2.
3.
4.
5.
Lenga mas papiá ku e persona ei:
(marka e den e vak pa tur persona of relashon)
Papiamento
Hulandes
Tantu
Un otro
Papiamento
lenga
komo
hulandes
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Appendix G – The Grand Average waveforms of the age-related analysis
F3
Fz
F4
C3
Cz
C4
P3
Pz
P4
O1
Oz
O2
Amplitude (µV)
-8
-200
time (ms)
1000
10
The grand average waveforms of twelve representative electrode sites for the MLF violation (dotted line) and its correct
counterpart (solid line) of condition 1 (MLF+ / ML= Dutch/ Adj Dutch – Noun Pap) versus condition 7 (MLF- / ML=Pap /
Adj Dutch – Noun Pap. Zero on the time axis marks the onset of the picture and color presentation. Electrodes are arrayed
from most anterior (top) to most posterior (bottom) and from left to right as they were positioned on the scalp. Negativity is
plotted up.
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
F3
Fz
F4
C3
Cz
C4
P3
Pz
P4
O1
Oz
O2
Amplitude (µV)
-8
-200
97
time (ms)
1000
10
The grand average waveforms of twelve representative electrode sites for the MLF violation (dotted line) and its correct
counterpart (solid line) of condition 2 (MLF+ / ML= Dutch/ Adj Pap- Noun Dutch) versus condition 8 (MLF- / ML=Pap / Adj
Pap – Noun Dutch. Zero on the time axis marks the onset of the picture and color presentation. Electrodes are arrayed from
most anterior (top) to most posterior (bottom) and from left to right as they were positioned on the scalp. Negativity is plotted
up.
98
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
F3
Fz
F4
C3
Cz
C4
P3
Pz
P4
O1
Oz
O2
Amplitude (µV)
-8
-200
time (ms)
1000
10
The grand average waveforms of twelve representative electrode sites for the MLF violation (dotted line) and its correct
counterpart (solid line) of condition 5 (MLF+ / ML= Pap / Noun Pap – Adj Dutch) versus condition 3 (MLF- / ML=Pap / Noun
Pap – Adj Dutch). Zero on the time axis marks the onset of the picture and color presentation. Electrodes are arrayed from
most anterior (top) to most posterior (bottom) and from left to right as they were positioned on the scalp. Negativity is plotted
up.
M. Wildeboer: An EEG study on Dutch-Papiamento code-switching production
F3
Fz
F4
C3
Cz
C4
P3
Pz
P4
O1
Oz
O2
Amplitude (µV)
-8
-200
99
time (ms)
1000
10
The grand average waveforms of twelve representative electrode sites for the MLF violation (dotted line) and its correct
counterpart (solid line) of condition 6 (MLF+ / ML= Pap / Noun Dutch – Adj Pap) versus condition 4 (MLF- / ML=Pap / Noun
Dutch - Adj Pap. Zero on the time axis marks the onset of the picture and color presentation. Electrodes are arrayed from most
anterior (top) to most posterior (bottom) and from left to right as they were positioned on the scalp. Negativity is plotted up.t